Contributions to Regional Haze in 

the Northeast and Mid-Atlantic 

United States 

Mid-Atlantic/Northeast Visibility Union 

(MANE-VU) Contribution Assessment 

Prepared by NESCAUM 

For the Mid-Atlantic/Northeast Visibility Union (MANE-VU) 

August 2006

Figure III-21 2002 Annual Average PM2.5, Sulfate, Nitrate and Total Carbon for MANE-VU 

based on IMPROVE and STN data. Mass data are supplemented by the FRM 

network

CONTRIBUTION OF RPO TO PM SO4 IN CLASS I AREAS

0

10

20

30

40

50

60

70

80

90

100

Brigantine, NJ Acadia, ME Lye Brook, VT Shenandoah,

VA

Percent MANE-VU LADCO VISTAS CenRAP Esurface

Contribution to PM Sulfate in Brigantine, NJ

CT

0.5%

LA

0.3%

MS

0.3%

MO

0.9%

AR

0.2%

ME

0.1%

IA

0.6%

MN

0.5%

TX

0.7%

MA

1.0%

SC

0.9%

WI

1.3%

AL

1.2%

GA

1.6%

NH

0.3%

DC

0.0%

RI

0.0%

VT

0.0%

DE

2.0%

TN

1.6%

IL

2.2%

KY

2.2%

NC

2.9%

MI

2.9% NJ

3.5%

IN

3.9%

VA

4.0%

MD

4.0%

WV

4.1%

NY

4.3%

OH

9.2%

PA

11.2%

Other US Elevated 

Emission

1.9%

Surface Emission + 

All Canada & Mexico 

Emission

30%

AIRS vs. CMAQ in PA,MD,DE,DC,VA,WV,NC during August 9~16, 2002

0

25

50

75

100

-75 -50 -25 0 25 50 75

Fractional Bias (%)

Fractional Error (%) . .

O3 hourly

O3 daytime hourly

O3 diurnal max

O3 daily mean

15%/35%

30%/50%

60%/75%
Members of NESCAUM Board 

Arthur Marin, Executive Director 

NESCAUM 

Anne Gobin, Bureau Chief 

Connecticut Department of Environmental Protection, Bureau of Air Management 

James P. Brooks, Bureau Director 

Maine Department of Environmental Protection, Bureau of Air Quality 

Barbara Kwetz, Director 

Massachusetts Department of Environmental Protection, Bureau of Waste Prevention 

Robert Scott, Director 

New Hampshire Department of Environmental Services, Air Resources Division 

William O’Sullivan, Director 

New Jersey Department of Environmental Protection, Office of Air Quality Management 

David Shaw, Director 

New York Department of Environmental Conservation, Division of Air Resources

Stephen Majkut, Chief 

Rhode Island Department of Environmental Management, Office of Air Resources 

Richard A. Valentinetti, Director 

Vermont Department of Environmental Conservation, Air Pollution Control Division 
ii

Contributions to Regional Haze in 

the Northeast and Mid-Atlantic 

United States 

Mid-Atlantic/Northeast Visibility Union 

(MANE-VU) Contribution Assessment 

Prepared by 

NESCAUM 

August 2006
iii

Contributions to Regional Haze in the Northeast 

and Mid-Atlantic United States 

Mid-Atlantic/Northeast Visibility Union (MANE-VU) 

Contribution Assessment 

Project Director

 Gary Kleiman, NESCAUM 

Program Officer

 Marcia Spink, U.S. EPA 

Editors

 John Graham, NESCAUM 

 Gary Kleiman, NESCAUM 

 Marika Tatsutani, NESCAUM 

 Paul Miller, NESCAUM 

Principal Contributors (listed alphabetically by chapter)

Chapter 1: Gary Kleiman, NESCAUM 

Chapter 2: Gary Kleiman, NESCAUM 

Chapter 3: George Allen, John Graham, Alan Leston, NESCAUM 

Chapter 4: Michael Borucke, John Graham, NESCAUM, Serpil Kayin, 

MARAMA, Jung-Hun Woo, NESCAUM 

 

Chapter 5: Serpil Kayin, MARAMA Iyad Kheirbek, Gary Kleiman, 

NESCAUM, Rich Poirot, VT DEC. 

Chapter 6: John Graham, Shan He, Emily Savelli, Jung-Hun Woo, 

NESCAUM 

Chapter 7: Mark Garrison, ERM, Dan Riley, Paul Wishinski, VT DEC 

Chapter 8: All of the above 

Chapter 9: Gary Kleiman, NESCAUM 
iv

Acknowledgments 

NESCAUM gratefully acknowledges the support of the United States Environmental 

Protection Agency. Funding for this report was provided through the MidAtlantic/Northeast Visibility Union (MANE-VU) Regional Planning Organization (Grant 

No. X-983799-01-0) to the Ozone Transport Commission. 

This report is the result of a joint effort by staff of the MANE-VU and its member states. 

NESCAUM also thanks the following individuals and organizations for providing 

comments on this report: 

Tom Downs, Maine Department of Environmental Protection 

VISTAS 

MOG 

Dominion Power 

AMC 

Ann McWilliams, EPA Region 1 

Printed: October 2006 
v
vi

TABLE OF CONTENTS 

Acknowledgments.............................................................................................................. iv 

Units, Symbols, Acronyms ................................................................................................ xi 

Executive Summary......................................................................................................... xiv 

1. Introduction................................................................................................................ 1-1 

2. Conceptual Model of Regional Haze in the MANE-VU Region .............................. 2-1 

2.1. Visibility Effects of Particulate Matter (PM).................................................... 2-1 

2.2. Chemical Composition of Particulate Matter in MANE-VU ........................... 2-1 

2.3. Geographic Considerations and Attribution of PM/Haze Contributors............ 2-2 

2.4. Seasonal differences.......................................................................................... 2-5 

2.5. Implications for control strategies .................................................................... 2-7 

2.6. Summary......................................................................................................... 2-10 

3. Overview of Monitoring Results ............................................................................... 3-1 

3.1. Baseline Conditions .......................................................................................... 3-1 

3.1.1. Preview of revised IMPROVE Algorithm for aerosol extinction............... 3-5 

3.2. 2002 Monitoring Data....................................................................................... 3-9 

3.2.1. Sulfate ......................................................................................................... 3-9 

3.2.2. Southwest-Northeast Gradient.................................................................. 3-10 

3.2.3. Seasonality ................................................................................................ 3-12 

3.2.4. Seasonal Mechanisms ............................................................................... 3-13 

3.3. RAIN data ....................................................................................................... 3-15 

4. Haze-Associated Pollutant Emissions........................................................................ 4-1 

4.1. Emissions Inventory Characteristics................................................................. 4-1 

4.1.1. Sulfur Dioxide (SO2)................................................................................... 4-1 

4.1.2. Volatile Organic Compounds (VOC) ......................................................... 4-2 

4.1.3. Oxides of Nitrogen (NOX) .......................................................................... 4-5 

4.1.4. Primary Particulate Matter (PM10 and PM2.5)............................................. 4-7 

4.1.5. Ammonia Emissions (NH3) ........................................................................ 4-9 

4.2. Contribution Assessments Based on Emissions Inventories .......................... 4-12 

4.2.1. Sulfur Dioxide Emissions Divided by Distance ....................................... 4-12 

4.2.2. Emissions times Upwind Probability........................................................ 4-17 

5. Data Analysis Techniques.......................................................................................... 5-1 

5.1. Trajectory Analysis........................................................................................... 5-1 

5.1.1. Incremental Probability............................................................................... 5-2 

5.1.2. Clustered Back-Trajectories........................................................................ 5-3 

5.1.3. Cluster-Weighted Probability ..................................................................... 5-4 

5.2. Source Apportionment Models and Ensemble Trajectory Analysis of Source 

Apportionment Results ................................................................................................ 5-5 

5.3. Trajectory Model Evaluation and Future Work................................................ 5-7 
vii

6. Chemical Transport Models....................................................................................... 6-1 

6.1. Chemical Transport Model (CTM) platforms – Overview............................... 6-1 

6.2. Preliminary Results........................................................................................... 6-3 

7. Lagrangian Dispersion Models.................................................................................. 7-1 

7.1. Platform Overview............................................................................................ 7-2 

7.2. CALPUFF Modeling Results for Individual Sources....................................... 7-2 

7.3. CALPUFF Modeling Results Overview........................................................... 7-3 

7.4. CALPUFF Results for Ranked State Sulfate Contributions............................. 7-3 

7.5. Future work and potential uses of CALPUFF results for BART determinations.. 

 ......................................................................................................................... 7-11 

8. Synthesis of Results Using Different Source Assessment Techniques ..................... 8-1 

8.1. Ranked Contribution......................................................................................... 8-1 

9. Conclusion ................................................................................................................. 9-1 

Appendix A: Application of Trajectory Analysis Methods to Sulfate Source Attribution 

Studies in the Northeast U.S. 

Appendix B: Source Attribution by Receptor-Based Methods 

Appendix C: Chemical Transport Model Results for Sulfate Source Attribution Studies in 

the Northeast U.S. 

Appendix D: Development of Parallel CALPUFF Dispersion Modeling Platforms for 

Sulfate Source Attribution Studies in the Northeast U.S. 
viii

FIGURES 

Figure 2-1. Summer time at Mt Washington ................................................................... 2-7 

Figure 2-2. Wintertime in Boston .................................................................................... 2-7 

Figure 3-1. Comparison of Old and New Algorithms for Baseline Worst Days............. 3-7 

Figure 3-2. Comparison of Old and New Algorithms for Baseline Worst Days............. 3-8 

Figure 3-3. New Jersey Urban Area Compared to an Upwind Background Site ............ 3-9 

Figure 3-4. MANE-VU FRM PM2.5 statistics along a southwest to northeast axis....... 3-10 

Figure 3-5. 2002 Seasonal average SO4 based on IMPROVE and STN data............... 3-11 

Figure 3-6. 2002 Annual average PM2.5, sulfate, nitrate and total carbon for MANE-VU 

based on IMPROVE and STN data. Mass data are supplemented by the FRM 

network................................................................................................................ 3-11 

Figure 3-7. Moving 60-day average of fine aerosol mass concentrations based on longterm data from two northeastern cities................................................................ 3-12 

Figure 3-8. 30-day average fine aerosol mass concentrations from eight northeastern 

cities .................................................................................................................... 3-12 

Figure 3-9. Mean hourly fine aerosol concentrations during the summer season ......... 3-14 

Figure 3-10. Mean hourly fine aerosol concentrations during the winter season.......... 3-14 

Figure 3-11. 2-Hour Reconstructed scattering at Acadia, Maine using semi-continuous 

SO4 and OC data for the third quarter of 2004 ................................................... 3-19 

Figure 4-1. State Level Sulfur Dioxide Emissions .......................................................... 4-3 

Figure 4-2. SO2 (Bar graph: Percentage fraction of four source categories, Circle: Annual 

emissions amount in 106

 tons per year) ................................................................ 4-3 

Figure 4-3. VOC (Bar graph: Percentage fraction of four source categories, Circle: 

Annual emissions amount in 106

 tons per year).................................................... 4-4 

Figure 4-4. State Level Nitrogen Oxides Emissions........................................................ 4-6 

Figure 4-5. NOX (Bar graph: Percentage fraction of four source categories, Circle: 

Annual emissions amount in 106

 tons per year).................................................... 4-6 

Figure 4-6. State Level Primary PM10 Emissions ............................................................ 4-8 

Figure 4-7. State Level Primary PM2.5 Emissions............................................................ 4-8 

Figure 4-8. Primary PM10 (Bar graph: Percentage fraction of four source categories, 

Circle: Annual emissions amount in 106 tons per year)...................................... 4-10 

Figure 4-9. Primary PM2.5 (Bar graph: Percentage fraction of four source categories, 

Circle: Annual emissions amount in 106 tons per year) ..................................... 4-10 

Figure 4-10. State Level Ammonia Emissions .............................................................. 4-11 

Figure 4-11. NH3 (Bar graph: Percentage fraction of four source categories, Circle: 

Annual emissions amount in 106 tons per year).................................................. 4-12 

Figure 4-12. Ranked state percent sulfate contributions to Northeast Class I receptors 

based on emissions divided by distance (Q/d) results......................................... 4-16 

Figure 4-13. Ranked state percent sulfate contributions to Mid-Atlantic Class I receptors 

based on emissions divided by distance (Q/d) results......................................... 4-16 

Figure 4-14. Ranked state percent sulfate contributions to Northeast Class I receptors 

based on emissions times upwind probability (E x UP) results.......................... 4-19 

Figure 4-15. Ranked state percent sulfate contributions to Mid-Atlantic Class I receptors 

based on emissions times upwind probability (E x UP) results.......................... 4-19 
ix

Figure 5-1. Incremental Probability (Top 10% Sulfate) at Acadia, Brigantine and Lye 

Brook 2000-2004 .................................................................................................. 5-3 

Figure 5-2. Incremental Probability (Bottom 10% Sulfate) at Acadia, Brigantine and Lye 

Brook 2000-2004 .................................................................................................. 5-3 

Figure 5-3. Proximity based cluster with the highest associated sulfate value for three 

sites in the MANE-VU region, Acadia (sulf=3.19 μg/m3), Brigantine 

(sulf=6.79 μg/m3), and Lye Brook (sulf=3.92 μg/m3) .......................................... 5-4 

Figure 5-4. Cluster Weighted Probability at Acadia, Brigantine and Lye Brook 2000-

2004....................................................................................................................... 5-5 

Figure 5-5. Sulfate characteristics of “secondary sulfate” (coal) sources identified at 

eastern sites ........................................................................................................... 5-6 

Figure 5-6. Incremental Probabilities for "Secondary Sulfate" (Coal) Sources in Eastern 

U.S......................................................................................................................... 5-7 

Figure 5-7. Comparison of probability fields for observed sulfate, “sulfate” source 

profiles for seven eastern sites and reconstructed deciviews................................ 5-7 

Figure 5-8. Comparison of IP contours generated by ATAD and HYSPLIT (both EDAS 

and FNL) for sulfate, nickel and selenium at Lye Brook...................................... 5-8 

Figure 5-9. ATAD Transport Layer Depth (TLD) by month. Color indicates the length of 

time prior to arriving at the receptor. .................................................................... 5-9 

Figure 6-1. Modeling domains used in NESCAUM air quality modeling studies. ......... 6-2 

Figure 6-2(a) and (b): CMAQ Integrated SIP Modeling Platform simulation results for 

2002, 2009 and 2018 relative to Uniform Progress Goals calculated according to 

current USEPA guidance ...................................................................................... 6-3 

Figure 6-3. REMSAD modeling tagging schemes. ........................................................ 6-5 

Figure 6-4. 2002 Eastern states’ contribution to annual PM sulfate in Acadia, ME ....... 6-6 

Figure 6-5. 2002 Eastern states’ contribution to annual PM sulfate in Brigantine, NJ ... 6-6 

Figure 6-6. 2002 Eastern states’ contribution to annual PM sulfate in Lye Brook, VT .. 6-7 

Figure 6-7. 2002 Eastern states’ contribution to annual PM sulfate in Shenandoah, VA6-7 

Figure 6-8. Comparison of Sulfate Extinctions on 20% Worst Visibility Days.............. 6-8 

Figure 7-1. CALPUFF modeling domain utilized by MANE-VU .................................. 7-1 

Figure 7-2. Correlation between MM5-based source contributions (Maryland/ERM) and 

NWS/rawindsonde-based source contributions (VT DEC) for common EGUs 

modeled at four receptor sites in or near MANE-VU........................................... 7-4 

Figure 7-3a. Ranked state percent sulfate contributions to Northeast Class I receptors 

based on observation-based (VT) CALPUFF results............................................ 7-9 

Figure 7-4a. Ranked state percent sulfate contributions to Northeast Class I receptors 

based on MM5-based (MD) CALPUFF results.................................................. 7-10 

Figure 8-1(a-d). Comparison of normalized (percent contribution) results using different 

techniques for ranking state contributions to sulfate levels at the MANE-VU Class 

I sites ..................................................................................................................... 8-7 

Figure 8-2. Estimated RPO contributions to sulfate concentrations at Class I areas using 

different assessment techniques .......................................................................... 8-10 

Figure 8-3. Ranked contributions of states to ambient sulfate concentrations at Acadia 

National Park, Maine. ......................................................................................... 8-11 

Figure 8-4. Ranked contributions of states to ambient sulfate concentrations at Brigantine 

Wilderness Area, New Jersey. ............................................................................ 8-12 
x

TABLES 

Table 3-1. Fine mass and percent contribution for 20% worst days................................ 3-2 

Table 3-2. Particle extinction and percent contribution for 20% worst days................... 3-2 

Table 3-3. Natural background and baseline calculations for select Class I areas .......... 3-2 

Table 3-4. Percent particle Bext reduction needed to meet uniform progress .................. 3-3 

Table 3-5. Mass reductions required on 20% worst days based on extinction estimates in 

Table 3-4 ............................................................................................................... 3-4 

Table 3-6. Estimated Mass Reduction on an Average Day ............................................. 3-5 

Table 3-7. Aerosol extinction by specie for 20% worst days .......................................... 3-6 

Table 3-8. Aerosol extinction by specie for 20% best days............................................. 3-6 

Table 4-1. 2002 SO2 CALPUFF-scaled Emissions over Distance Impact (μg/m3)....... 4-15 

Table 4-2. 2002 SO2 Upwind Probability (percent contribution).................................. 4-18 

Table 7-1. CALPUFF Overall Modeling Summary ........................................................ 7-3

Table 7-2a. Sulfate Ion Impacts by State (Annual Average)........................................... 7-5 

Table 8-1. Annual Average Sulfate Impact from REMSAD (%).................................... 8-2 

Table 8-2. Annual Average Sulfate Impact from Q/D (%).............................................. 8-3

Table 8-3. Annual Average Sulfate Impact from CALPUFF (NWS Observations) (%)8-4 

Table 8-4. Annual Average Sulfate Impact from CALPUFF (MM5) (%) ...................... 8-5 

Table 8-5. Annual Average Sulfate Impact from percent time upwind method (%)....... 8-6 

Table 8-6. Ranked Contributing States to Acadia Sulfate ............................................... 8-9 
xi

Units, Symbols, Acronyms 

Acronyms 

AGL – Above Ground Level 

ATAD – Atmospheric Transport and 

Diffusion Model 

ARL – Air Resources Laboratory (NOAA) 

BART – Best Available Retrofit 

Technology 

BEIS – Biogenic Emission Inventory 

System 

BRAVO - Big Bend Regional Aerosol and 

Visibility Observational study 

CAIR – Clean Air Interstate Rule 

CALMET – Meteorological model for 

developing input data for CALPUFF 

CALPUFF – Lagrangian dispersion model 

developed by EarthTech, Inc. 

CAMNET – Northeast Visibility Camera 

Network 

CASTNet – Clean Air States and Trends 

Network 

CEMS – Continuous Emissions 

Monitoring System 

CENRAP – Central Regional Air Planning 

Association 

CFR – Code of Federal Regulations 

CMAQ – Community Multi-scale Air 

Quality Model 

CMB – Chemical Mass Balance 

CMU – Carnegie Mellon University 

CTM – Chemical Transport Model 

CWP – Clustered Weighted Probability 

EDAS – Eta Data Assimilation System 

EFIG – USEPA Emission Factor and 

Inventory Group 

EGU – Electricity Generating Unit 

EMAD – Emissions, Monitoring and 

Analysis Division 

ERM – Environmental Resources 

Management, Inc. 

FASTNET – Fast Aerosol Sensing and 

Tools for Natural aErosol Tracking 

FNL – FiNaL run of the Global Data 

Assimilation System 

FRM – Federal Reference Method 

GIS – Geographic Information System 

IMPROVE – Interagency Monitoring of 

Protected Visual Environments 

IP – Incremental Probability 

HAPS – Hazardous Air Pollutants 

HYSPLIT – Hybrid Single-Particle 

Langrangian Integrated Trajectory 

model 

MANE-VU – Mid-Atlantic/Northeast 

Visibility Union 

MARAMA – Mid Atlantic Region Air 

Management Association 

MDE – Maryland Department of the 

Environment 

MDNR – Maryland Department of Natural 

Resources 

MM5 – Fifth Generation Mesoscale Model 

MOBILE – Mobile Source Emission 

Factor Model (USEPA) 

MWRPO – Midwest Regional Planning 

Organization 

NAAQS – National Ambient Air Quality 

Standards 

NARSTO – North American Research 

Strategy for Tropospheric Ozone 

NCAR – National Center for Atmospheric 

Research 

NEI – National Emissions Inventory 

NESCAUM – Northeast States for 

Coordinated Air Use Management 

NET – National Emissions Trends (EPA) 

NOAA – National Oceanic and 

Atmospheric Administration 

NRC – National Research Council 

NTI – National Toxics Inventory 

NWS – National Weather Service 

OAQPS – USEPA Office of Air Quality 

Planning and Standards 
xii

OAR – USEPA Office of Air and 

Radiation 

OTC – Ozone Transport Commission 

PCA – Principle Component Analysis 

PM – Particulate Matter 

PMF – Positive Matrix Factorization 

PSCF – Potential Source Contribution 

Function 

RAIN – Real Time Aerosol Intensive 

Network 

REMSAD – Regulatory Modeling System 

for Aerosols and Deposition 

RH – Relative Humidity 

RPO – Regional Planning Organization 

RTA – Residence Time Analysis 

SIP – State Implementation Plan 

SMOKE – Sparse Matrix Operator Kernel 

Emissions model 

STN – Speciation Trends Network 

TLD – Transport Layer Depth 

TSC – Technical Support Committee 

UMD – University of Maryland 

UNMIX – Mathematical receptor model 

used for source attribution studies 

USEPA – United States Environmental 

Protection Agency 

USFS – United States Forest Service 

USFWS – United States Fish and Wildlife 

Service 

USNPS – United States National Park 

Service 

VISTAS - Visibility Improvement State 

and Tribal Association of the Southeast 

VT DEC – Vermont Department of 

Environmental Conservation 

WRAP-Western Regional Air Partnership 

Chemical Species 

BC – Black Carbon 

CM – coarse mass 

CO – carbon monoxide 

EC – elemental carbon 

HC – hydrocarbons 

H2SO4 – sulfuric acid 

HNO3 – nitric acid 

NOX – oxides of nitrogen (NO2 and NO) 

NO – nitric oxide 

NO2 – nitrogen dioxide 

NO3

- – nitrate 

NH3 – ammonia 

NH4 – ammonium 

NH4HSO4 – ammonium bisulfate 

(NH4)2SO4 – ammonium sulfate 

(NH4NO3) – ammonium nitrate 

O3 – ozone 

OC – organic carbon 

OMC – organic mass from carbon 

PM2.5 – particle matter up to 2.5 μm in size 

PM10 – particle matter up to 10 μm in size 

Se – selenium 

SOA – secondary organic aerosol 

SO2 – sulfur dioxide 

SO4

2- – sulfate 

VOC – volatile organic compounds 

Symbols 

bext – light extinction coefficient (Mm-1) 

Ci – constant for wind sector i 

d – distance 

E x UP – emissions times upwind 

probability 

f(RH) – relative humidity adjustment 

factor 

I – impact 

Q – annual emissions 

R

2

 – correlation coefficient 
xiii

Units 

Length

m – meter 

μm – micrometer (0.000001m; 10-6m)

km – kilometer (1,000 x m; 103 m) 

Mm – Megameter (1,000,000 x m; 106 m) 

Area

m

2

 – square meter 

km2 – square kilometer 

Volume

L – liter 

m

3

 – cubic meter 

Concentration

μg/m3 – micrograms per cubic meter 

ng/m3 – nanograms per cubic meter 

ppb – parts per billion 

ppm – parts per million 

Scattering Efficiency

m

2

/g – square meters per gram 

Visibility

dv – deciview 
xiv

Executive Summary 

Regional haze State Implementation Plans (SIPs) due in December 2007 must 

include a contribution assessment and pollution apportionment analysis as part of the 

long-term emissions management strategy for meeting visibility improvement objectives 

in Class I areas subject to USEPA’s 1999 Regional Haze Rule. The MidAtlantic/Northeast Visibility Union (MANE-VU) Technical Support Committee (TSC) 

has adopted a weight-of-evidence approach as a first step toward meeting these 

obligations and in an effort to better understand the causes of visibility impairment at 

Class I areas within the MANE-VU region. The weight-of-evidence approach relies on 

several independent methods for assessing the contribution of different emissions sources 

and geographic source regions to regional haze in the northeastern and mid-Atlantic 

portions of the United States. 

The preliminary findings described in this report draw from the considerable body 

of work that has already been developed concerning the nature and extent of visibility 

impairment in the MANE-VU region. This work has produced a conceptual model of 

regional haze in which sulfate emerges as the most important single constituent of hazeforming fine particle pollution and the principle cause of visibility impairment across the 

region. Sulfate alone accounts for anywhere from one-half to two-thirds of total fine 

particle mass on the 20 percent haziest days at MANE-VU Class I sites. Even on the 20 

percent clearest days, sulfate generally accounts for the largest fraction (40 percent or 

more) of total fine particle mass in the region. Sulfate has an even larger effect when one 

considers the differential visibility impacts of different particle constituents. It typically 

accounts for 70–82 percent of estimated particle-induced light extinction at northeastern 

and mid-Atlantic Class I sites. 

While substantial visibility impairment is common across the region, it is most 

severe in the southern and western portions of MANE-VU that are closest to large power 

plant sources of sulfur dioxide (SO2) emissions located in the Ohio River and Tennessee

Valleys. Summertime visibility is driven almost exclusively by the presence or absence 

of regional sulfate, whereas wintertime visibility depends on a combination of regional 

and local influences coupled with local meteorological conditions (inversions) that can 

lead to the concentrated build-up of emissions from local sources. 

These findings suggest that an effective emissions management approach would 

rely heavily on broad-based regional SO2 control efforts in the eastern United States 

aimed at reducing summertime fine particulate matter (PM2.5) concentrations. MANEVU is investigating additional measures to reduce in-region emissions of SO2 and organic 

carbon (OC), which is typically the next most important contributor to overall fine 

particle mass throughout the region. Nearby SO2 reductions can help reduce wintertime 

PM concentrations, while OC reductions can help reduce total PM concentrations yearround. For areas with high wintertime PM levels, strategies aimed at reducing ambient 

levels of nitrogen oxides (NOX) may also be effective. 

Available monitoring data provide strong evidence that regional SO2 reductions 

have yielded, and will continue to yield, reductions in ambient secondary sulfate levels 

with subsequent reductions in regional haze and associated light extinction. They indicate 

that reductions in anthropogenic primary particle emissions will also result in visibility 
xv

improvements, but that these will not have a zone of influence as large as those of the 

secondary aerosols. 

Given the dominant role of sulfate in the formation of regional haze in the 

Northeast and Mid-Atlantic region — and the likelihood that SO2 reductions will 

therefore need to play a central role in achieving near-term visibility improvements — 

this report focuses on early efforts to assess the regional sulfate contribution to ambient 

fine particle levels experienced at the (primarily rural) MANE-VU Class I areas. The 

primary objective of this report is to identify and describe the suite of analytical tools and 

techniques that are presently available for: (1) understanding the causes of sulfate-driven 

visibility impairment at Class I areas in MANE-VU and nearby regions, as well as the 

relative contribution of various emissions sources and geographic source regions; and (2) 

describe how these tools and techniques will be applied in future MANE-VU SIP work. 

The analytical and assessment tools discussed in this report include Eulerian 

(grid-based) source models, Lagrangian (air parcel-based) source dispersion models, as 

well as a variety of data analysis techniques that include source apportionment models, 

back trajectory calculations, and the use of monitoring and inventory data. A range of 

methodological approaches characterize these tools, which Table ES-1 summarizes. The 

tools rely on different data sources and entail varying degrees of sophistication and 

uncertainty. Thus, it is important to emphasize that these methods have been extensively 

reviewed, updated, and refined over the past year to ensure that the highest quality results 

are now available for the SIP development process. The overall coherence and 

consistency of results that emerges from application of these tools and techniques suggest 

that what is known about the causes of sulfate pollution in the MANE-VU region is 

sufficiently robust to provide a useful and appropriate basis for design of future control 

programs and for consultations between different regional organizations charged with 

planning for compliance with the Regional Haze Rule. 

Figure ES-1 provides one illustration of the high degree of correspondence in the 

results. The figure shows rankings of state contributions to sulfate mass at Brigantine 

Wilderness Area in New Jersey derived from several of the techniques listed in Table ES1.1

 There is substantial consistency across a variety of analysis methods using techniques 

based on disparate chemical, meteorological and physical principles. Taken together, 

these findings create a strong weight-of-evidence case for the preliminary identification 

of the most significant contributors to visibility impairment in the MANE-VU Class I 

areas. 

Similar results for other sites demonstrate that highly simplified, empirical 

approaches for identifying source contributions are consistent with more sophisticated 

approaches. Therefore, a firm basis exists for addressing contributions to regional 

transport of sulfate, and the range of variability between these techniques suggests the 

precision of these estimates. 

 

1

 As described in Chapter 8, REMSAD is the only analysis platform used to quantify “out of domain” 

contributions to sulfate. Thus, the REMSAD calculated contribution for the “out of domain” sources (17% 

at Brigantine, NJ) was used to calculate the percent contribution shown in Figure ES-1 for all other 

methods. 
xvi

We have further aggregated these results by regional planning organization (RPO) 

using state-by-state sulfate mass contributions (in μg/m3) derived by the REMSAD, 

CALPUFF, emissions/distance, and emissions times (×) upwind probability methods.2 

Figure ES-2 shows these results in terms of their absolute contribution (displayed within 

the bars shown in the graphic) and in terms of their proportional contribution relative to 

other RPOs.1 

Table ES-1. Summary of technical approaches for attributing state contributions to 

observed sulfate in MANE-VU Class I areas. 

Analytical technique Approach 

Emissions/distance Empirical 

Incremental probability Lagrangian trajectory technique 

Cluster-weighted probability Lagrangian trajectory technique 

Emissions × upwind probability Empirical/trajectory hybrid 

Source apportionment approaches Receptor model/trajectory hybrid 

REMSAD tagged species Eulerian source model 

CALPUFF with MM5-based meteorology Lagrangian source dispersion model 

CALPUFF with observation-based meteorology Lagrangian source dispersion model 

 

2

 See Chapter 4 for an explanation of how the emissions divided by distance technique is expressed as a 

sulfate mass concentration and the associated assumptions for the emissions × upwind probability method. 

Figure ES-1. Comparison results using different techniques for ranking state 

contributions (in units of percent of in-domain contribution) to sulfate levels at 

Brigantine Wilderness Area, New Jersey. 

0

2

4

6

8

10

12

14

16

18

OTHERPAOHWVMDCANAVANCNYNJINKYGADEILMITNCENRSCMAALWICTNHMEDCMSRI

REMSAD

Q/D

CALPUFF (VT)

CALPUFF (MD)

%UW
xvii

Notwithstanding small differences in precisely which states were included within 

each assessment technique, estimates obtained from averaging over the five quantitative 

assessment techniques indicate that MANE-VU states account for about 25-30 percent of 

the sulfate in the Acadia, Brigantine, and Lye Brook Class I areas. The Midwest RPO 

(MWRPO) and Visibility Improvement State and Tribal Association of the Southeast 

(VISTAS) states each account for about 15 percent of the total sulfate contribution at 

Acadia and about 25 percent each at Brigantine and Lye Brook. The Central states 

Regional Air Partnership (CENRAP) states, Canada, and an “out of domain” contribution 

add the remainder.3 Although variation exists across estimates of contributions for 

different sites and using different techniques, the overall pattern is generally consistent. 

Shenandoah National Park, Virginia, which is a VISTAS Class I area, has a 

somewhat reversed order of relative contributions. There, VISTAS and MWRPO states 

account for roughly 30 percent of overall sulfate each, with MANE-VU states 

contributing roughly 15-20 percent and CENRAP states, Canada and “out of domain” 

accounting for the remainder. 

 

3

 Note here that the contribution representing out of domain sources was – in all cases – derived solely by 

the REMSAD platform and that this value has been applied to the other analysis techniques to provide a 

consistent estimate of the total contributions to sulfate pollution at each site. 

Figure ES-2. Estimated RPO contributions to sulfate concentrations at Class I areas 

using different assessment techniques 

0.73

0.65 1.03

0.58

1.20

0.89 1.37 1.07 0.68

0.56 0.83 0.59

0.81 0.56 0.83

0.96

0.24

0.40 0.60

0.49

0.63

0.61

0.94 0.58 0.46 0.57 0.77

0.52

1.05 0.94 1.22 0.88

0.18 0.26 0.39

0.45

0.77 0.87 1.04

0.84

0.29

0.36 0.51

0.42

1.36 1.11 1.49 1.25

0.02 0.05 0.09

0.05

0.04

0.07 0.10

0.05

0.04

0.07 0.12

0.05

0.06 0.08 0.09

0.07

0.18 0.19 0.29 0.35

0.24

0.08 0.11 0.20

0.27

0.12 0.17

0.28

0.19 0.08 0.11

0.26

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

Acadia Brigantine Lye Brook Shenendoah

MANEVU MIDWEST VISTAS CENRAP CANADA OUTSIDE DOMAIN
xviii

Other qualitative analysis methods have been developed that reinforce the 

findings shown above. These include trajectory methods and source apportionment 

techniques. These receptor-based methods provide compelling support for the more 

quantitative attribution methods discussed previously. Figure ES-3 (left panel) shows the 

source region associated with a “coal combustion/secondary sulfate” source profile 

observed at Brigantine Wilderness in New Jersey and (right panel) the predominant 

meteorological pathways associated with the highest sulfate observations at Brigantine. 

The meteorological transport regime most common during high sulfate observations 

(shown on the right) directly connects the most likely source region with the receptor site 

(shown on the left), which reinforces the large quantitative contributions of source states 

determined for the Brigantine receptor in Chapter 8. 

Finally, we note that while sulfate is the most important particle constituent for 

designing near-term control strategies, reductions in other local and distant pollutant 

emissions are important. Additional measures will be necessary in the long term to 

address public health impacts of ambient fine particle concentrations and to achieve longterm regional haze goals to restore pristine visibility conditions year-round in the nation’s 

Class I wilderness areas. This is especially true during winter months, when planners 

need to give particular consideration to reducing urban and mobile sources of NOX and 

OC as well as sources of SO2.

Figure ES-3. Geographic regions associated with “coal combustion/secondary sulfate” 

sources (left) and sulfate transport (right) for Brigantine Wilderness Area, NJ. 

Note: This figure is the consistency of interpretation between the “coal-combustion/secondary 

sulfate” source region and receptor site shown in the left hand panel being directly connected by 

the predominant meteorological transport pathway on high observed sulfate days at Brigantine, 

shown in the right hand panel. 
xix
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 1-1

1. INTRODUCTION 

The 1999 Regional Haze Rule (hereafter, the Haze Rule) requires States and 

Tribes to submit State Implementation Plans (SIPs) to the U.S. Environmental Protection 

Agency (USEPA) for approval by January 2008 at the latest. The haze SIPs must 

include a “contribution assessment” to identify those states or regions that may be 

influencing specially protected federal lands known as Federal Class I areas.4 These 

states or regions would then be subject to the consultation provisions of the Haze Rule. 

The Haze Rule also requires a “pollution apportionment” analysis as part of the long-term 

emissions management strategy for each site. 

In 2004, Congress harmonized the timeline for SIP submissions, including SIPs 

for meeting federal fine particulate matter (PM2.5) and regional haze requirements.5

 One 

effect of this change is that the “regional planning SIP” or “committal SIP” — originally 

due one year after PM designations — will now be due along with all other SIP products 

in late 2007 or early 2008. 

The Haze Rule originally would have applied a very low threshold test to 

determine whether a state would be part of a regional planning process, As a result of the 

congressional harmonization, however, the requirement for a contribution assessment is 

now, in effect, part of the “pollution apportionment” analysis used to determine which 

sources must be included in a long-term emissions management strategy. This is subject 

to a somewhat higher threshold of evidence since it forms the basis for judging whether 

long-term strategies are adequately addressing the causes of haze in protected areas. 

To adequately determine the degree to which specific geographic regions or areas 

are contributing to visibility impairment at MANE-VU Class I areas, the MANE-VU 

Technical Support Committee (TSC) has adopted a weight-of-evidence approach that 

relies on several independent methods of attribution. These include Eulerian (grid-based) 

source models, Lagrangian (air pollution-based) source dispersion models, and a variety 

of data analysis techniques that include source apportionment models, back trajectory 

calculations, and the use of monitoring and inventory data. 

 

4

 The Class I designation applies to national parks exceeding 6,000 acres, wilderness areas and national 

memorial parks exceeding 5,000 acres, and all international parks that were in existence prior to 1977. In 

the MANE-VU area, this includes: Acadia National Park, Maine; Brigantine Wilderness (within the Edwin 

B. Forsythe National Wildlife Refuge), New Jersey; Great Gulf Wilderness, New Hampshire; Lye Brook 

Wilderness, Vermont; Moosehorn Wilderness (within the Moosehorn National Wildlife Refuge), Maine; 

Presidential Range – Dry River Wilderness, New Hampshire; and Roosevelt Campobello International 

Park, New Brunswick. 

5

 In the Omnibus Appropriations Act of 2004 [Consolidated Appropriations Act for Fiscal Year 2004, Pub. 

L. 108–199, January 23, 2004], Congress harmonized both designations and regional haze SIP deadlines. 

EPA promulgated PM2.5 designations for all areas of each state on December 17, 2004. The Omnibus 

Appropriations Act provides that regional haze SIPs for each state as a whole are then due not later than 

three years after promulgation of the PM2.5 designations. Thus, all components of the regional haze SIPs are 

now due no later than December 17, 2007 (three years after the USEPA issued the official designations). 

The USEPA has suggested informally that they will accept Regional Haze SIPs in April 2008 when PM2.5

SIPs are due. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 1-2

While we already know much about visibility impairment and its causes in the 

MANE-VU region (see NESCAUM, 2001; NESCAUM, 2002), significant gaps in 

understanding remain with respect to the organic component of fine particulate pollution. 

While we expect continuing research activities to substantially benefit future SIP efforts, 

the MANE-VU members have determined that sufficient information exists to design 

effective emission control strategies to meet visibility goals through 2018. 

Reducing sulfur emissions offers particular leverage for achieving near-term 

visibility goals. It is the sulfate fraction of airborne fine particle matter that dominates 

light extinction on the 20 percent worst visibility days in the Northeast and Mid-Atlantic 

region. This is important because improving visibility on the 20 percent worst days is a 

near-term regulatory objective under the Regional Haze rule. In addition, many tools are 

available for assessing sulfate contributions. Therefore, this document focuses to a large 

extent on assessing sources and source regions for the sulfate fraction of haze-causing 

particles. 

To lay a foundation for the analyses described in later chapters of this report, 

Chapter 2 provides a conceptual model of visibility impairment in the eastern United 

States. Chapter 3 presents a summary of available monitoring data and observations that 

we use to support the conceptual model and to validate models and data analyses. In fact, 

measured data — far from being used merely to support modeling analyses — serve as 

the primary basis for several of the receptor techniques presented in later chapters. There 

is thus no substitute for a robust monitoring network to understand the causes of fine 

particle pollution and visibility impairment. 

Later chapters reinforce the notions introduced in Chapters 2 and 3 in using 

emission inventories (Chapter 4), receptor-based approaches including the use of back 

trajectories, trajectory clustering techniques and source apportionment models (Chapter 

5), Eulerian chemical transport models (Chapter 6), and Lagrangian dispersion models 

(Chapter 7). We synthesize and interpret these various techniques in Chapter 8 and 

present conclusions in Chapter 9. We discuss technical aspects of the analyses in several 

of these later chapters in greater detail in a series of appendices. 

As a general matter throughout this report, the focus is on assessing the 

contribution of all sources within broad geographical areas (i.e., whole states) whose 

combined emissions are likely to contribute to regional haze. As cited in Watson (2002), 

the National Research Council (NRC) has concluded that: 

(1) “...a program that focuses solely on determining the contribution of 

individual emission sources to visibility impairment is doomed to failure. 

Instead, strategies should be adopted that consider many sources 

simultaneously on a regional basis, although assessment of the effect of 

individual sources will remain important in some situations;” (2) “...there 

are (and will probably continue to be) considerable uncertainties in 

ascertaining a precise relationship between individual sources and the spatial 

pattern of regional haze;” and (3) “...the best approach for evaluating 

emission sources is a nested progression from simpler and more direct 

models to more complex and detailed methods” (Watson, 2002). 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 1-3

Watson (2002) goes on to point out that, “Part of the modeling conundrum is the 

focus of modeling efforts on demonstrating attainment rather than gaining a better 

understanding of the situation. Although USEPA emphasizes the construction of a 

conceptual model and evaluation of the weight of evidence in its introduction, the 

modeling details contained in the guidance are business as usual: seeking a quantitative 

comparison of present and future design values with a numerical goal.” 

Consistent with the NRC’s admonition and USEPA’s stated desire to incorporate 

weight-of-evidence approaches to improve conceptual models, MANE-VU has attempted 

wherever possible to incorporate qualitative analyses in sensible ways so as to increase 

confidence in its quantitative estimates of the contribution of various emissions sources 

and source regions to regional haze. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 1-4

References 

NESCAUM, Regional Haze and Visibility Impairment in the Northeast and Mid-Atlantic 

United States, Northeast States for Coordinated Air Use Management, Boston, MA, 

January, 2001. 

NESCAUM, Updated Visibility Statistics for the MANE-VU Region Technical 

Memorandum #1, Northeast States for Coordinated Air Use Management, Boston, MA 

February, 2002. 

Watson, J., “Visibility: Science and Regulation,” JAWMA 52:628-713, 2002. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-1

2. CONCEPTUAL MODEL OF REGIONAL HAZE IN THE 

MANE-VU REGION 

Developing a conceptual model of regional haze requires combining experience 

and atmospheric-science expertise with multiple data sources and analysis techniques. 

This includes measured data on ambient pollutant concentrations as well as emission 

inventory and meteorological data, chemical transport modeling, and observationally 

based models (NARSTO, 2003). Here, we begin with a conceptual model based on the 

existing scientific literature concerning fine particles and their effect on visibility. This 

includes numerous review articles and reports on the subject. Most past assessments of 

fine particle pollution and visibility impairment have tended to be national in scope. For 

purposes of this discussion, we have selectively reviewed the literature in order to present 

a distinctly Eastern focus. 

Because the uncertainties involved in any particular method of analysis are 

usually large or ill-defined, it is preferable to develop visibility and fine particle 

management strategies with inputs from multiple analyses using multiple approaches. 

The MANE-VU TSC has adopted this approach, which leads to the diversity of data 

analyses and model results that follow. Later chapters of this report use original 

contributions and analyses developed by MANE-VU researchers to bolster and support 

the concepts presented in these introductory chapters. MANE-VU has combined the 

outputs and integrated them into a final conceptual model that explains the formation and 

transport mechanisms for fine particulate matter in the eastern United States. 

2.1. Visibility Effects of Particulate Matter (PM) 

Visibility impairment in the eastern United States is largely due to the presence of 

light-absorbing and light-scattering fine particles in the atmosphere. The USEPA has 

identified visibility impairment as the best understood of all environmental effects of air 

pollution (Watson, 2002). A long-established physical and chemical theory relates the 

interaction of particles and gases in the atmosphere with the transmission of visual 

information along a sight path from object to observer. 

Visibility-impairing particle-light interactions are sensitive to the chemical 

composition of the particles involved, and also depend strongly on ambient relative 

humidity. Secondary particles, which form in the atmosphere through chemical reactions, 

tend to fall within a size range that is most effective at scattering visible light (NARSTO, 

2003). These particles are generally smaller than one micrometer (μm) or one onemillionth of a meter. The particles that contribute most to visibility impairment also are a 

concern under the health-based National Ambient Air Quality Standard (NAAQS) for 

fine particulate matter, defined as including all particles with an aerodynamic diameter 

less than 2.5 μm (PM2.5). 

2.2. Chemical Composition of Particulate Matter in MANE-VU 

Sulfate alone accounts for anywhere from one-half to two-thirds of total fine 

particle mass on the 20 percent haziest days at all MANE-VU Class I sites. Even on the 

20 percent clearest days, sulfate generally accounts for the largest fraction (40 percent or 

more) of total fine particle mass in the region (NESCAUM, 2001). Sulfate accounts for a 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-2

major fraction of PM2.5, not only in the Northeast but across the eastern United States 

(NARSTO, 2003). 

After sulfate, organic carbon (OC) consistently accounts for the next largest 

fraction of total fine particle mass. Its contribution typically ranges from 20 to 30 percent 

of total fine particle mass on the haziest days. The fact that the contribution from organic 

carbon can be as high as 40 percent at the more rural sites on the 20 percent clearest days 

is likely indicative of the role played by organic emissions from vegetation (so-called 

“biogenic hydrocarbons” (HC)). Relative contributions to overall fine particle mass from 

nitrate (NO3), elemental carbon, and fine soil are all smaller (typically under 10 percent), 

but the relative ordering among the three species varies with location. Nitrate plays a 

noticeably more important role at urban sites compared to northeastern and mid-Atlantic 

Class I locations, perhaps reflecting a greater contribution from vehicles and other urban 

pollution sources (NESCAUM, 2001). 

Almost all particle sulfate originates from sulfur dioxide (SO2) oxidation and 

typically associates with ammonium (NH4) in the form of ammonium sulfate 

((NH4)2SO4), 95 percent of SO2 emissions are from anthropogenic sources (primarily 

from fossil fuel combustion), while the majority of ammonium comes from agricultural 

activities and, to a lesser extent, from transportation sources in some areas (NARSTO, 

2003). 

Two major chemical pathways produce sulfate from SO2 in the atmosphere. In 

the gas phase, production of sulfate involves the oxidation of SO2 to sulfuric acid 

(H2SO4), ammonium bisulfate (NH4HSO4), or ammonium sulfate, depending on the 

availability of ammonia (NH3). In the presence of small wet particles (typically much, 

much smaller than rain drops or even fog), a highly efficient aqueous phase process can 

oxidize SO2 to sulfate extremely quickly (~10 percent per hour). 

Not only is sulfate the dominant contributor to fine particle mass in the region, it 

accounts for anywhere from 60 percent to almost 80 percent of the difference between 

fine particle concentrations on the clearest and haziest days at northeastern and midAtlantic Class I sites. Notably, at urban locations such as Washington, DC, sulfate 

accounts for only about 40 percent of the difference in average fine particle 

concentrations for the 20 percent most versus least visibility impaired days (NESCAUM, 

2001). We discuss this further in the next section of this chapter. 

Some of the dominant components of total fine particle mass have an even larger 

effect when considering the differential visibility impacts of different particle species. 

Sulfate typically accounts for over 70 percent of estimated particle-induced light 

extinction at northeastern and mid-Atlantic Class I sites. Organic carbon continues to be 

the second most important contributor to particle-induced light extinction at rural sites on 

the most impaired days, but slips to third behind nitrate in Washington, DC (NESCAUM, 

2001). 

2.3. Geographic Considerations and Attribution of PM/Haze 

Contributors 

In the East, an accumulation of particle pollution often results in hazy conditions 

extending over thousands of square kilometers (km2

) (NARSTO, 2003). Substantial 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-3

visibility impairment is a frequent occurrence in even the most remote and pristine areas 

of the Northeast and Mid-Atlantic region (NESCAUM, 2001). 

Both annual average and maximum daily fine particle concentrations are highest 

near heavily industrialized areas and population centers. Not surprisingly, given the direct 

connection between fine particle pollution and haze, the same pattern emerges when one 

compares measures of light extinction on the most and least visibility impaired days at 

parks and wilderness areas subject to the Haze Rule in the Northeast and Mid-Atlantic 

region (NESCAUM, 2001). 

Contributions to fine particle mass concentrations at rural locations include longrange pollutant transport as well as non-anthropogenic background contributions. Urban 

areas generally show mean PM2.5 levels exceeding those at nearby rural sites. In the 

Northeast, this difference implies that local urban contributions are roughly 25 percent of 

the annual mean urban concentrations, with regional aerosol contributing the remaining, 

and larger, portion (NARSTO, 2003). 

This rural versus urban difference in typical concentrations also emerges in a 

source apportionment analysis of fine particle pollution in Philadelphia (Chapter 10, 

NARSTO, 2003) using two different mathematical models, UNMIX and Positive Matrix 

Factorization (PMF). (We describe these models in greater detail in Chapter 5 and 

Appendix B.) This analysis provides additional insight concerning sources of fine 

particle pollution in urban areas of the densely populated coastal corridor between 

Washington D.C. and New England. Specifically, this analysis found the following 

apportionment of PM2.5 mass in the study area: 

• Local SO2 and sulfate: ~ 10 percent 

• Regional sulfate: ~ 50 percent 

• Residual oil: 4-8 percent 

• Soil: 6–7 percent 

• Motor vehicles: 25–30 percent 

The analysis does not account for biogenic sources, which most likely are 

embedded in the motor vehicle fraction (NARSTO, 2003). The Philadelphia study 

suggests that both local pollution from near-by sources and transported “regional” 

pollution from distant sources contribute to the high sulfate concentrations observed in 

urban locations along the East Coast on an annual average basis. Summertime sulfate 

and organic carbon are strongly regional in eastern North America. Typically 75–95 

percent of the urban sulfate concentrations and 60–75 percent of the urban OC 

concentrations arise from cumulative region-wide contributions (NARSTO, 2003). 

While these statistics provide some preliminary context for attributing 

responsibility for the region’s particulate matter and visibility problems, they say nothing 

about the relative efficiency of a state’s or region’s emissions in causing or contributing 

to the problem. It is clear that distance from the emissions source matters. Local, nearby sources are exceedingly important and sources within about 200 kilometers (km) are 

much more efficient (on a per ton emitted basis) at producing pollution impacts at eastern 

Class I sites such as Shenandoah National Park than emissions sources farther away 

(USNPS, 2003). In general, the “reach” of sulfate air pollution resulting from SO2
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-4

emissions is longest (650-950 km). The reach of ammonia emissions or reduced nitrogen 

relative to nutrient deposition is the shortest (around 400 km), while oxides of nitrogen 

and sulfur — in terms of their impacts with respect to acidic deposition — have a reach 

between 550–650 km and 600–700 km, respectively (USNPS, 2003). 

Monitoring evidence indicates that non-urban visibility impairment in eastern 

North America is predominantly due to sulfate particles, with organic particles generally 

second in importance (NARSTO, 2003). This makes sense, given the “long reach” of 

SO2 emissions once they are chemically transformed into sulfate and given the ubiquitous 

nature of OC sources in the East. 

The poorest visibility conditions occur in highly industrialized areas 

encompassing and adjacent to the Ohio and Tennessee River Valleys. These areas 

feature large coal-burning power stations, steel mills, and other large emissions sources. 

Average visibility conditions are also poor in the highly populated and industrialized 

mid-Atlantic seaboard but improve gradually northeast of New York City (Watson, 

2002). 

A review of source apportionment and ensemble trajectory analyses conducted by 

USEPA (2003) found that all back trajectory analyses for Eastern sites associated sulfate 

with the Ohio River Valley area. Studies also frequently associated other types of 

industrial pollutants with known source areas. Several studies in the USEPA review 

noted transport across the Canadian border, specifically sulfates from the midwestern 

United States into Canada, and smelter emissions from Canada into the northeastern 

United States. 

A recent, comprehensive analysis of air quality problems at Shenandoah National 

Park conducted by the U.S. National Park Service (USNPS, 2003) focused on 

contributions to particulate pollution and visibility impairment south of the MANE-VU 

region. In descending order of importance, the National Park Service analysis 

determined that Ohio, Virginia, West Virginia, Pennsylvania, and Kentucky comprise the 

top five of thirteen key states contributing to ambient sulfate concentrations and haze 

impacts at the park. West Virginia, Ohio, Virginia, Pennsylvania, and Kentucky comprise 

the top five contributing states with respect to sulfur deposition impacts at the park. 

Finally, Virginia, West Virginia, Ohio, Pennsylvania, and North Carolina were found to 

be the top five states contributing to deposition impacts from oxidized nitrogen at the 

park (USNPS, 2003). 

In summary, the National Park Service found that emission sources located within 

a 200 kilometer (125 mile) radius of Shenandoah cause greater visibility and acidic 

deposition impacts at the park, on a per ton basis, than do more distant emissions sources 

(USNPS, 2003). When mapping deposition and concentration patterns for all three 

pollutants using contour lines, the resulting geographic pattern shows a definite eastward 

tilt in the area of highest impact. This is the result of prevailing wind patterns, which 

tend to transport most airborne pollutants in an arc from the north-northeast to the east.6

 

The Park Service found, for example, that emissions originating in the Ohio River Valley 

end up three times farther to the east than to the west (USNPS, 2003). 

 

6

 The prevailing winds are eastward to northeast. This leads to greater pollution transport to the eastnortheast relative to other directions. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-5

We note that several MANE-VU states may themselves be contributing to fine 

particle mass concentrations observed at Shenandoah. According to the Park Service 

analysis, sources in Pennsylvania contribute on the order of 10 percent of observed 

ambient sulfate mass at the park, while sources in Maryland, New York and Delaware 

contribute 3.5, 1.7 and 0.5 percent respectively (USNPS, 2003). 

2.4. Seasonal differences 

Eastern and western coastal regions of the United States and Canada show marked 

seasonality in the concentration and composition of fine particle pollution, while central 

interior regions do not (NARSTO, 2003). While the MANE-VU domain extends inland 

as far as the Pennsylvania and Ohio border, the majority of Class I areas in MANE-VU 

cluster along the East Coast and thus typically show strong seasonal influences. 

Maximum PM2.5 concentrations occur during the summer over most of the Northeast, 

with observed summer values for rural areas in the region, on average, twice those of 

winter. Winter nitrate concentrations, however, are generally higher than those observed 

in summer and, as mentioned above, urban concentrations typically exceed rural 

concentrations year-round. In addition, local mobile source carbon grows in importance 

during wintertime. Hence, in some large urban areas such as Philadelphia and New York 

City, peak concentrations of PM2.5 can occur in winter. 

The conceptual models that explain elevated regional PM2.5 peak concentrations 

in the summer differ significantly from models that explain the largely urban peaks 

observed during winter. On average, summertime concentrations of sulfate in the 

northeastern United States are more than twice that of the next most important fine 

particle constituent, OC, and more than four times the combined concentration of nitrate 

and black carbon (BC) constituents (NARSTO, 2003). Episodes of high summertime 

sulfate concentrations are consistent with stagnant meteorological flow conditions and the 

accumulation of airborne sulfate (via atmospheric oxidation of SO2) through long-range 

transport of sulfur emissions from industrialized areas within and outside the region. 

National assessments (NARSTO, 2003) have indicated that in the winter, sulfate 

levels in urban areas are almost twice as high as background sulfate levels across the 

eastern U.S., indicating that the local urban contribution to wintertime sulfate levels is 

comparable in magnitude to the regional sulfate contribution from long-range transport. 

MANE-VU’s network analysis for the winter of 2002 suggests that the local 

enhancement of sulfate in urban areas of the OTR is somewhat less with ranges from 25 

to 40% and that the long range transport component of PM sulfate is still the dominant 

contributor in most eastern cities. 

In the winter, urban OC and sulfate each account for about a third of the overall 

PM2.5 mass concentration observed in Philadelphia and New York City. Nitrate also 

makes a significant contribution to urban PM2.5 levels observed in the northeastern 

United States during the winter months. Wintertime concentrations of OC, sulfate, and 

NO3 in urban areas can be twice the average regional concentrations of these pollutants, 

indicating the importance of local source contributions (NARSTO, 2003). This is likely 

because winter conditions are more conducive to the formation of local inversion layers 

that prevent vertical mixing. Under these conditions, emissions from tailpipe, industrial 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-6

and other local sources become concentrated near the Earth’s surface, adding to 

background pollution levels associated with regionally transported emissions. 

It is worth noting that while sulfate plays a significant role in episodes of elevated 

particle pollution during summer and winter months, the processes by which sulfate 

forms may vary seasonally. Nearly every source apportionment study reviewed by 

USEPA (2003) identified secondary sulfate originating from coal combustion sources as 

the largest or one of the largest contributors to overall fine particle mass in the region. It 

often accounted for more than 50 percent of PM2.5 mass at some locations during some 

seasons. In a few cases, source apportionment studies identified a known local source of 

sulfate, but most assessments (in conjunction with back trajectory analysis) have pointed 

to coal-fired power plants in the Midwest as an important source for regional sulfate. 

Studies with multiple years of data have also tended to identify a distinguishable 

chemical “signature” for winter versus summer sources of sulfate, with the summer 

version typically accounting for a greater share of overall fine particle mass. Researchers 

have speculated that the two profiles represent two extremes in the chemical 

transformation processes that occur in the atmosphere between the source regions where 

emissions are released and downwind receptor sites. We note that while coal combustion 

is often referred to as the “sulfate source” because of the dominance of its sulfate 

contribution, coal combustion is usually the single largest source of selenium (Se) and 

other heavy metal trace elements (USEPA, 2003). 

Visually, hazy summer days in the Northeast can appear quite different from hazy 

winter days. The milky, uniform visibility impairment shown in Figure 2-1 is typical of 

summertime regional haze events in the Northeast. During the winter, by comparison, 

reduced convection and the frequent occurrence of shallow inversion layers often creates 

a layered haze with a brownish tinge, as shown in Figure 2-2. This visual difference 

suggests seasonal variation in the relative contribution of different gaseous and particle 

constituents during the summer versus winter months (NESCAUM, 2001). Rural and 

inland areas tend not to experience these layered haze episodes as frequently due to the 

lack of local emission sources in most rural areas (valleys with high wood smoke 

contributions are an exception). 

Overall (regional) differences in summer versus winter particle mass 

concentrations and corresponding visibility impairment (as measured by light extinction) 

are largely driven by seasonal variation in sulfate mass concentrations. This is because 

winter meteorological conditions are less conducive to the oxidation of sulfate from SO2

(as borne out by the previously cited source apportionment studies). In addition, seasonal 

differences in long-range transport patterns from upwind SO2 source regions may be a 

factor. 

The greater presence of nitrate during the cold season is a consequence of the 

chemical properties of ammonium nitrate. Ammonia bonds more weakly to nitrate than it 

does to sulfate, and ammonium nitrate tends to dissociate at higher temperatures. 

Consequently, ammonium nitrate becomes more stable at lower temperatures and hence 

contributes more to overall light extinction during the winter months (NESCAUM, 

2001). 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-7

2.5. Implications for control strategies 

A 2003 assessment of fine particulate matter by NARSTO7

 notes that, “[c]urrent 

air-quality management approaches focusing on reductions of emissions of SO2, NOX,

and VOCs are anticipated to be effective first steps towards reducing PM2.5 across North 

America, noting that in parts of California and some eastern urban areas VOC (volatile 

organic compounds) emissions could be important to nitrate formation.” 

This conclusion seems to be well supported by the historical record, which 

documents a pronounced decline in particulate sulfate concentrations across the eastern 

United States during the 1990s. The timing of this observed decline suggests that this is 

linked to reductions in SO2 emissions resulting from controls implemented under the 

federal Acid Rain Program beginning in the early to mid 1990s. From 1989 to 1998, SO2

 

7

 NARSTO was formerly an acronym for the "North American Research Strategy for Tropospheric Ozone." 

More recently, the term NARSTO became simply a wordmark signifying a tri-national, public-private 

partnership for dealing with multiple features of tropospheric pollution, including ozone and suspended 

particulate matter. For more information on NARSTO see http://www.cgenv.com/Narsto/. 

Figure 2-1. Summer time at Mt Washington 

 Clean Day Typical Haze Event 

 

Figure 2-2. Wintertime in Boston 

 Clean Day Typical Haze Event 

 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-8

emissions in the eastern half of the country — that is, including all states within a region 

defined by the western borders of Minnesota and Louisiana — declined by about 25 

percent. This decline in SO2 emissions correlated with a decline of about 40 percent in 

average SO2 and sulfate concentrations, as measured at Clean Air States and Trend 

Networks (CASTNet) monitoring sites in the same region over the same time period. In 

fact, at prevailing levels of atmospheric SO2 loading, the magnitudes of the emissions and 

concentration changes were not statistically different. This finding suggests that regional 

reductions in SO2 emissions have produced near-proportional reductions of particulate 

sulfate in the eastern United States (NARSTO, 2003). Reductions since 1990 in 

precursor SO2 emissions are likely also responsible for a continued decline in median 

sulfate concentrations in the northeastern United States. Nevertheless, the fact that 

episodes of high ambient sulfate concentrations (with peak levels well above the regional 

median or average) continue to occur, especially during the summertime when regional 

transport from the Ohio River Valley is also at its peak, suggests that further reductions in 

regional and local SO2 emissions would provide significant further air quality and 

visibility benefits (NARSTO, 2003). 

For urban areas of the northeastern and southeastern United States, an effective 

emissions management approach may be to combine regional SO2 control efforts aimed 

at reducing summertime PM2.5 concentrations with local SO2 and OC control efforts. 

Local SO2 reductions would help reduce wintertime PM concentrations, while OC 

reductions can help reduce overall PM concentrations year-round. For areas with high 

wintertime PM levels, strategies that involve NOX reductions may also be effective 

(NARSTO, 2003). 

Further support for this general approach may be found in a review of several 

studies by Watson (2002) that concluded SO2 emission reductions have in most cases 

been accompanied by statistically significant reductions in ambient sulfate 

concentrations. One study (Husar and Wilson, 1993) shows that regionally averaged light 

extinction closely tracks regionally averaged SO2 emissions for the eastern United States 

from 1940 through the mid-1980s. Another study by Malm et al. (2002) shows that 

regionally averaged emissions and ambient concentrations decreased together from 1988 

through 1999 over a broad region encompassing the states of Connecticut, Delaware, 

Illinois, Indiana, Kentucky, Maine, Massachusetts, Maryland, Michigan, New 

Hampshire, New Jersey, New York, Ohio, Pennsylvania, Rhode Island, Vermont, 

Virginia, Wisconsin, and West Virginia (Watson, 2002). 

These studies and available data from the IMPROVE (Interagency Monitoring of 

Protected Visual Environment) monitoring network provide strong evidence that regional 

SO2 reductions have yielded, and will continue to yield, reductions in ambient secondary 

sulfate levels with subsequent reductions in regional haze and associated light extinction. 

They indicate that reductions in anthropogenic primary particle emissions will also result 

in visibility improvements, but that these will not have a zone of influence as large as 

those of the secondary aerosols (Watson, 2002). 

Watson (2002) notes that during the 65 years in which the regional haze program 

aims to reach its final visibility goals, several opportunities to revise this basic control 

approach will arise through the decadal SIP cycle. This enables new scientific results to 

continue to exert a positive influence as states implement new regulatory control 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-9

programs for SO2, NOX and VOCs, and as ambient concentrations of these pollutants 

change relative to each other and relative to ambient ammonia levels. As these 

relationships between species change, atmospheric chemistry may dictate a revised 

control approach to those previously described. Further research on these issues should 

be a priority for supporting 2018 SIP submissions. They include the possibility that: 

• Reduction of sulfate in a fully neutralized atmosphere (excess ammonia) 

could encourage ammonium nitrate formation. 

• Ever greater emissions reductions could be required to produce a given 

level of improvement in ambient pollutant concentrations because of nonlinearities in the atmospheric formation of sulfate. 

• Changes in ambient conditions favoring the aqueous oxidation of sulfate 

(this pathway largely accounts for the non-linearity noted above) may 

have implications for future emissions control programs. Causes of 

changing ambient conditions could include, for example, climate change. 

West et al. (1999) examine a scenario for the eastern United States where PM2.5

mass decreases linearly with ammonium sulfate until the latter is fully neutralized by 

ammonia. Further reductions would free ammonia for combination with gaseous nitric 

acid that, in turn, would slightly increase PM2.5 until all of the nitric acid is neutralized. 

At that point, further sulfate reductions would once again be reflected in lower PM2.5

mass. This is an extreme case that is more relevant to source areas (e.g., Ohio) where 

nitric acid (HNO3) is more abundant than in areas with lower emissions (e.g., Vermont) 

(Watson, 2002). 

In most situations with non-neutralized sulfate (typical of the eastern United 

States), ammonia is a limiting agent for the formation of nitrate but will not make any 

difference until sulfate is reduced to the point where it is completely neutralized. At that 

point, identifying large sources of ammonia emissions will be important. This point is 

likely to be many years in the future, however (Watson, 2002). 

Based on analyses using the Community Multi-Scale Air Quality (CMAQ) model, 

the aqueous phase production of sulfate in the Northeast appears to be very oxidant 

limited and hence non-linear. Thus, conditions that are conducive to a dominance of the 

gas-phase production pathway drive the summer peaks in ambient sulfate levels. 

Nonetheless, the expected reduction in ambient sulfate levels resulting from a given 

reduction in SO2 emissions is less than proportional overall due to the non-linearity 

introduced by the aqueous pathway for sulfate formation (NARSTO, 2003). These nonlinearity effects are more pronounced for haze than for sulfate deposition, especially at 

higher sulfate air concentrations (USNPS, 2003). 

Finally, we note that because visibility in the clearest areas is sensitive to even 

minute increases in particle concentrations, strategies to preserve visibility on the clearest 

days may require stringent limits on emissions growth. In this context, even the dilute 

emissions from distant sources can be important (NARSTO, 2003). 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-10

2.6. Summary 

The presence of fine particulate matter in ambient air significantly obscures 

visibility during most parts of the year at sites across the MANE-VU region. Particle 

pollution generally, and its sulfate component specifically, constitute the principle driver 

for regional visibility impacts. While the broad region experiences visibility impairment, 

it is most severe in the southern and western portions of MANE-VU that are closest to 

large power plant SO2 sources in the Ohio River and Tennessee Valleys. 

The presence or absence of regional sulfate almost exclusively drives summer 

visibility impairment, whereas winter visibility depends on a combination of regional and 

local influences coupled with local meteorological conditions (inversions) that lead to the 

concentrated build-up of pollution. 

Sulfate is the key particle constituent from the standpoint of designing control 

strategies to improve visibility conditions in the northeastern United States. Significant 

further reductions in ambient sulfate levels are achievable, though they will require more 

than proportional reductions in SO2 emissions. 

Long-range pollutant transport and local pollutant emissions are important, 

especially along the eastern seaboard, so one must also look beyond the achievement of 

further sulfate reductions. During the winter months, in particular, consideration also 

needs to be given to reducing urban sources of SO2, as well as NOX and OC (NARSTO, 

2003). 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-11

References 

Husar, R.B. and Wilson, W.E., “Haze and Sulfur Emission Trends in the Eastern United 

States,” Environ. Sci. Technol. 27 (1), 12-16, 1993. 

NARSTO, Particulate Matter Science for Policy Makers: A NARSTO Assessment, EPRI 

1007735, February, 2003. 

NESCAUM, Regional Haze and Visibility Impairment in the Northeast and Mid-Atlantic 

United States, Northeast States for Coordinated Air Use Management, Boston, MA, 

January, 2001. 

Malm, W. C., Schichtel, B. A., Ames, R.B., and Gebhart, K.A., “A 10-year spatial and 

temporal trend of sulfate across the United States,” J. Geophys. Res. 107(D22):4627, 

doi:10.1029/2002JD002107, 2002. 

USEPA, COMPILATION OF EXISTING STUDIES ON SOURCE APPORTIONMENT 

FOR PM2.5, Second Draft, Emissions, Monitoring, and Analysis Division, Office of Air 

Quality Planning and Standards, Research Triangle Park, NC, August, 2003. 

USNPS, Assessment of Air Quality and Related Values in Shenandoah National Park, 

Technical Report NPS/NERCHAL/NRTR-03/090, U.S. Department of the Interior, 

National Park Service, Northeast Region, Philadelphia, Pennsylvania, May, 2003. 

Watson, J., “Visibility: Science and Regulation,” JAWMA 52:628-713, 2002. 

West, J.J., Ansari, A.S., and Pandis, S.N., “Marginal PM2.5: Nonlinear Aerosol Mass 

Response to Sulfate Reductions in the Eastern United States,” JAWMA 49:1415-1424, 

1999. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 2-12
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-1

3. OVERVIEW OF MONITORING RESULTS 

SIP developers use monitoring data in three important ways to support regional 

haze SIP activities. Section 3.1 presents measurements from the IMPROVE network 

needed in establishing SIP requirements. Following USEPA guidance (USEPA, 2003a; 

USEPA, 2003b), we use these data to preview the uniform progress goals that SIP 

developers must consider for each Class I area. 

Section 3.2 reviews a recent NESCAUM report (NESCAUM, 2004b) to 

demonstrate how available monitoring data support and validate the conceptual model 

presented in Chapter 2. 

Section 3.3 presents early results from the MANE-VU Real-Time Aerosol 

Intensive Network (RAIN). These suggest some of the ways MANE-VU is preparing to 

extend and improve understanding of visibility issues across the region. We anticipate 

this aspect of the MANE-VU monitoring strategy to be critical for future status reports 

and SIP updates. 

3.1. Baseline Conditions 

The Haze Rule requires states and tribes to submit plans that include calculations 

of current and estimated baseline and natural visibility conditions. They will use 

monitoring data from the IMPROVE program as the basis for these calculations. Table 

3-1 presents the five-year average8

 of the 20 percent worst day mass concentrations in six 

Class I areas. Five of these areas are in MANE-VU and one (Shenandoah) is nearby but 

located in a neighboring regional planning organization (RPO) region.9 Table 3-2 gives 

the corresponding worst day contributions to particle extinction for the six Class I areas. 

Each of these tables show the relative percent contribution for all six Class I sites. 

Sulfate and organic carbon dominate the fine mass, with sulfate even more important to 

particle extinction. 

To guide the states in calculating baseline values of reconstructed extinction and 

for estimating natural visibility conditions, USEPA released two documents in the fall of 

2003 outlining recommended procedures (USEPA 2003a; USEPA 2003b). These 

proposed methods were used, along with the data in Table 3-1 and Table 3-2 to create 

Table 3-3, which provides detail on the 20 percent worst conditions for the six Class 1 

areas. 

The first column of data in the Table 3-3 gives the default natural background 

levels for the worst visibility days at these six sites. Although debate continues with 

regard to some assumptions underlying the USEPA default approach for estimating 

natural background visibility conditions, MANE-VU has decided to use this approach, at 

least initially, for 2008 SIP planning purposes (NESCAUM, 2004a). The second column 

shows the baseline visibility conditions on the 20 percent worst visibility days. These 

values are based on IMPROVE data from the official five-year baseline period (2000-

 

8

 Great Gulf calculations are based on four years of data (2001-2004). 

9

 Note that values presented for Shenandoah, a Class I area in the Visibility Improvement State and Tribal 

Association of the Southeast (VISTAS) region, are for comparative purposes only. VISTAS will determine 

uniform rates of progress for areas within its region. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-2

2004). Using these baseline and natural background estimates, we derive the uniform 

rate of progress shown in the third column.10 The final column displays the interim 2018 

progress goal based on 14 years of improvement at the uniform rate. 

Table 3-1. Fine mass and percent contribution for 20% worst days 

20% Worst-day fine mass (μg/m3) / % contribution to fine mass 

Site SO4 NO3 OC EC Soil 

Acadia 6.3 / 60% 0.8 / 8% 2.5 / 23% 0.4 / 4% 0.5 / 5% 

Brigantine 11.5 / 59% 1.8 / 9% 4.5 / 23% 0.7 / 4% 1.0 / 5% 

Great Gulf 7.3 / 63% 0.3 / 3% 2.9 / 25% 0.4 / 3% 0.6 / 5% 

Lye Brook 8.5 / 62% 1.1 / 8% 3.0 / 22% 0.5 / 3% 0.6 / 5% 

Moosehorn 5.7 / 58% 0.7 / 7% 2.6 / 27% 0.4 / 4% 0.4 / 4% 

Shenandoah 13.2 / 72% 0.7 / 4% 3.3 / 18% 0.6 / 3% 0.7 / 4% 

Table 3-2. Particle extinction and percent contribution for 20% worst days 

20% Worst-day particle extinction (Mm-1) / % contribution to extinction

Site SO4 NO3 OC EC Soil CM 

Acadia 66.0 / 73% 8.1 / 9% 10.1 / 11% 4.4 / 5% 0.5 / 1% 1.8 / 2% 

Brigantine 106.2 / 69% 16.1 / 10% 18.3 / 12% 7.1 / 5% 1.0 / 1% 5.2 / 4% 

Great Gulf 66.5 / 76% 3.0 / 3% 10.6 / 13% 3.8 / 4% 0.5 / 1% 2.9 / 3% 

Lye Brook 76.7 / 73% 9.3 / 9% 12.1 / 11% 4.7 / 5% 0.7 / 1% 1.8 / 2% 

Moosehorn 56.1 / 70% 6.3 / 8% 10.5/ 13% 4.4 / 5% 0.4 / 0% 2.1 / 3% 

Shenandoah 132.5 / 82% 5.8 / 4% 13.2 / 8% 5.7 / 4% 0.8 / 0% 2.6 / 2% 

Table 3-3. Natural background and baseline calculations for select Class I areas 

Site 

Natural 

Background (dv) 

Baseline 

2000-04 (dv) 

Uniform Rate 

(dv/year) 

Interim Progress 

Goal 2018 (dv) 

Acadia 11.45 22.34 0.18 19.80 

Brigantine 11.28 27.60 0.27 23.97 

Great Gulf 11.30 22.25 0.18 19.69 

Lye Brook 11.25 23.70 0.21 20.80 

Moosehorn 11.36 21.18 0.16 18.89 

Shenandoah 11.27 27.88 0.28 24.00 

The regional haze rule calls for steady improvement of visibility on the 20 percent 

worst visibility days. States are to consider this uniform rate of progress, and if 

reasonable measures can be identified to meet or exceed this rate while ensuring no 

degradation of visibility on the best days, then it should be adopted as a Federal Class I 

 

10 We calculate the rate of progress as (baseline – natural background)/60 to yield the annual deciview (dv) 

improvement needed to reach natural background conditions in 2064, starting from the 2004 baseline. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-3

area’s reasonable progress goal. A number of instructive analyses are presented below 

using each area’s uniform progress goal as an example, but these should not be 

interpreted as constituting MANE-VU recommendations on reasonable progress goals. 

As a practical means of analyzing uniform progress goals, we have examined the 

components of observed fine particle pollution that substantially contribute to visibility 

degradation. This analysis shows that certain species dominate the extinction budget 

while others play virtually no role on the worst haze days. 

As demonstrated in Table 3-2, the inorganic constituents of fine particles (sulfates 

and nitrates) are the dominant contributors to visibility impairment, accounting for about 

80 percent of total particle extinction. Within the MANE-VU sites, the relative split 

between these two components is about eight to one sulfate to nitrate (at Shenandoah, the 

average 20 percent worst day contribution of sulfates is even more dominant). 

Carbonaceous components account for the bulk of the remaining particle extinction, 

ranging from 12 to nearly 20 percent, mostly in the form of organic carbon. The 

remaining components add little to the extinction budget on the worst days, with a few 

percent attributable to coarse mass and around a half percent from fine soil. 

 One approach to designing control strategies for achieving reasonable progress 

goals is to reduce all components of PM2.5 in equal proportion. Achieving the 2018 

uniform progress goals (expressed in Mm-1 in the second column of Table 3-4) requires 

between a 29 and 36 percent reduction in each component of the six haze components of 

fine particle extinction if their relative percent contributions to the current worst baseline 

conditions are kept constant (see the third column of Table 3-4). Given the dominant role 

of sulfate and nitrate, however, and the difficulty in obtaining 29 to 36 percent reductions 

in some of the other categories such as soil or course mass, sulfate- and nitrate-based 

control programs are likely to offer more reasonable emission reduction opportunities. 

 

11 We derive the information in this table from the results of Table 3-3. First, we converted the baseline 

and interim goal levels from dv to Mm-1 units, thus avoiding the logarithmic nature embedded into the 

deciview calculations. The first column of the table gives the difference between baseline and interim goal. 

The ratio of this difference to the baseline yields the uniform rate of reduction tabulated in the second 

column. We generate the paired species reduction percentages by using the wet and dry aerosol extinction 

coefficients. We determine f(RH) values by dividing the five-year Bext average by the dry extinction 

coefficient, giving a weighted average value of the f(RH) during the worst 20% of days. Similarly, in 

Table 3-5, we calculate mass values using the relative contributions of the species to be reduced and their 

wet and dry efficiencies. 

Table 3-4. Percent particle Bext reduction needed to meet uniform progress11

Site 

Particle Extinction 

Decrease (Mm-1) 

Uniform 

Reduction (%) 

Sulfate/Nitrate 

Reduction (%) 

OC/EC 

Reduction (%) 

Acadia 27.7 31 38 194 

Brigantine 55.3 36 46 218 

Great Gulf 30.6 33 42 195 

Lye Brook 35.4 34 41 210 

Moosehorn 23.4 29 38 158 

Shenandoah 57.1 36 42 303 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-4

The fourth column of Table 3-4 displays the results if a sulfate and nitrate focused 

control approach were taken to meet uniform progress goals. For these two inorganic 

species, a greater reduction would be necessary on the 20 percent worst days if the other 

four components showed no change relative to baseline levels. The last column shows 

that the contribution of the carbonaceous species is too small to meet the entire required 

2018 progress goal on its own (i.e. the percent reduction is greater than 100) if a carbononly control approach were attempted. 

Since it is easier to understand the implications of requisite mass reductions, 

rather than extinction, Table 3-5 tabulates the corresponding mass changes required for 

meeting uniform progress goals on the 20 percent worst days. On an absolute mass basis, 

the changes across sites are more varied than they are when viewed from a percentage 

change perspective. That in part is a function of the relative pollution levels at each site, 

in addition to the logarithmic nature of the deciview (dv). This table (along with Table 

3-6) can aid planners to gauge the potential impact that meeting uniform progress goals 

under the Regional Haze program will have on regional fine particle mass levels. 

Table 3-6 provides an estimate of mass decreases that might be expected on an 

average day. It assumes using either a uniform rate of change in all species, or a uniform 

rate of change in the sulfate and nitrate component of fine particulate, to achieve the 

progress toward the 2018 goals, respectively. These values are likely a lower bound to 

the annual average change at Class I areas anticipated from current conditions to 2018 as 

they are based on the assumption that on the best days, no change occurs and the percent 

reduction on the middle days is half of what is predicted on the worst.12

 

12 We derived the values tabulated in Figure 3-6 as follows: We multiplied half of the percentage change 

expected on the worst 20% of days by the average mass concentration of each species for the middle 20% 

of days. Note that if we apply a 25% reduction on the cleaner remaining quintile and 75% reduction on the 

dirtier remaining quintile, the annual average reduction would presumably be greater than that on the 

middle days given the skew in the distribution of all days. For example, in the inorganic-only case at 

Acadia, the average of the worst 20% change and best 20% is (2.69 + 0)/2 or 1.35 μg/m3

, which is nearly 

four times greater than the middle day. Further, given the large reduction on the worst days, it is 

reasonable to expect some small improvement on the best days. 

Table 3-5. Mass reductions required on 20% worst days based on extinction 

estimates in Table 3-4 

20% Worst Day Mass Reduction (μg/m3) 

Uniform Percent Change All Species Only Inorganic Only 

Carbonaceous 

Site SO4 NO3 OC EC SO4 NO3 OC EC 

Acadia 1.95 0.25 0.76 0.13 2.38 0.31 4.80 0.85 

Brigantine 4.14 0.65 1.64 0.26 5.22 0.82 9.92 1.56 

Great Gulf 2.42 0.11 0.97 0.13 3.06 0.14 5.74 0.76 

Lye Brook 2.85 0.36 1.02 0.16 3.49 0.44 6.36 1.00 

Moosehorn 1.68 0.20 0.77 0.13 2.14 0.26 4.12 0.69 

Shenandoah 4.78 0.24 1.19 0.21 5.57 0.28 9.94 1.74 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-5

Table 3-6. Estimated Mass Reduction on an Average Day 

Estimated Average Day Mass Reduction (μg/m3

) 

Uniform Percent Change All Species Only Inorganic 

Site SO4 NO3 OC EC SO4 NO3

Acadia 0.25 0.05 0.16 0.02 0.31 0.06 

Brigantine 0.80 0.19 0.38 0.08 1.01 0.25 

Great Gulf 0.28 0.04 0.19 0.03 0.36 0.05 

Lye Brook 0.29 0.07 0.17 0.03 0.36 0.09 

Moosehorn 0.25 0.05 0.19 0.03 0.32 0.06 

Shenandoah 0.79 0.24 0.28 0.05 0.92 0.28 

3.1.1. Preview of revised IMPROVE Algorithm for aerosol extinction 

Recently, the IMPROVE Steering Committee accepted an alternative approach 

for calculating visibility metrics based on measured aerosol concentrations. The new 

algorithm improves the correspondence between the reconstructed extinction and directly 

measured light scattering at the extremes of the visibility range. These extremes form the 

basis for determining the uniform progress “glide path.” 

The new equation revises or adds to the original version. The most significant 

changes include: 

• revision of the dry aerosol extinction coefficients for sulfate, nitrate and 

organic carbon, 

• splitting sulfate, nitrate and organic mass into small and large size fractions 

based on total species mass, 

• revised f(RH) curves for inorganic species, 

• inclusion of sea salt mass and associated f(RH) growth factor, 

• use of a site-specific Rayleigh scattering term, and 

• revision of the organic mass multiplier. 

The VIEWS website provides the revised dataset for all IMPROVE data, allowing 

the calculation of the baseline period with the new algorithm. Natural background 

calculation methods that mirror many of the changes adopted as an alternative for 

baseline calculations have been suggested; however, none have been formally adopted by 

the IMPROVE Steering Committee at this time. 

As a first step toward assessing the implications of the algorithm revisions, we 

compare the baseline visibility levels from the old and new approaches. The new 

calculation approach results in between one and two deciview increase in the 20 percent 

worst visibility conditions during the baseline period for the six sites considered. 

Extinction changes are observed for all components, with increases ranging from 6 to 42 

percent depending on species. The greatest overall percentage change occurs for organic 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-6

carbon and the least for fine soil. Changes in the baseline 20 percent best days were 

much less with the absolute contribution of a component to visibility degradation 

increasing in some cases and decreasing in others. On average, the values decrease by 

0.1 deciview. Table 3-7 and Table 3-8 summarize the species-specific changes for worst 

and best days’ aerosol extinction. 

Table 3-7. Aerosol extinction by specie for 20% worst days 

20% worst-day particle extinction (Mm-1) New Algorithm / Old Algorithm 

Site SO4 NO3 OC EC Soil Coarse Salt 

Acadia 76.4 / 66 8.6 / 8.1 12.5 / 10.1 4.8 / 4.4 0.6 / 0.5 2.1 / 1.8 1.4 / 0 

Brigantine 134.2 / 106.2 18.1 / 16.1 25.9 / 18.3 7.9 / 7.1 1.0 / 1.0 6.5 / 5.2 0.7 / 0 

Great Gulf 79.6 / 66.5 3.4 / 3.0 14.8 / 10.6 4.3 / 3.8 0.6 / 0.5 3.1 / 2.9 0.1 / 0 

Lye Brook 94.4 / 76.7 10 / 9.3 17.1 / 12.1 5.3 / 4.7 0.7 / 0.7 2.1 / 1.8 0.1 / 0 

Moosehorn 64 / 56.1 7 / 6.3 13.4 / 10.5 5.1 / 4.4 0.4 / 0.4 2.5 / 2.1 1.1 / 0 

Shenandoah 169.6 / 132.5 7.9 / 5.8 18.2 / 13.2 6.5 / 5.7 0.8 / 0.8 3.0 / 2.6 0.1 / 0 

Table 3-8. Aerosol extinction by specie for 20% best days 

20% best-day particle extinction (Mm-1) New Algorithm / Old Algorithm 

Site SO4 NO3 OC EC Soil Coarse Salt 

Acadia 6.8 / 7.4 1.1 / 1.2 2.3 / 2.4 0.9 / 0.9 0.1 / 0.1 0.7 / 0.7 0.4 / 0 

Brigantine 5.7 / 6.2 1.0 / 1.1 2.0 / 2.1 0.9 / 0.9 0.1 / 0.1 0.9 / 0.7 0.2 / 0 

Great Gulf 5.7 / 6.2 1.0 / 1.1 2.0 / 2.1 0.9 / 0.9 0.1 / 0.1 0.9 / 0.7 0.2 / 0 

Lye Brook 4.5 / 5.0 1.2 / 1.2 1.3 / 1.4 0.6 / 0.6 0.1 / 0.1 0.5 / 0.5 0.0 / 0 

Moosehorn 6.8 / 7.3 1.0 / 1.2 3.1 / 3.1 1.0 / 1.0 0.1 / 0.1 1.1 / 1.1 0.3 / 0 

Shenandoah 11.4 / 12.8 4.2 / 4.4 2.9 / 3.0 1.6 / 1.6 0.2 / 0.2 1.1 / 1.1 0.1 / 0 

Figure 3-1and Figure 3-2 graphically compare the old and new algorithm for six 

sites. The left-hand side of the figures presents the old contribution of aerosol extinction 

while the right-hand side shows the new calculations. Relatively small differences are 

apparent, with slight relative decreases in sulfate contribution offset by small increases in 

nitrate, organic carbon and the addition of sea salt. 

The potential impact of these changes on the uniform rate of progress slope 

cannot be determined at this time, since revisions in natural background calculations 

remain incomplete. A preliminary assessment, however, suggests that natural 

background estimates for MANE-VU may increase by about 10 percent. This translates 

to a change of just over one deciview. This estimate combined with the average increase 

of 1.5 deciview in baseline conditions would not likely change the slope of the uniform 

progress curve in any significant way. Nonetheless, the actual mass reductions required 

could change given the logarithmic nature of the haze index, where marginal mass 

changes are larger at higher deciview levels. It is not a straightforward exercise to 

estimate the potential effect of such changes given the increased complexity of the new 

algorithm relative to the old equation. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-7

Figure 3-1. Comparison of Old and New Algorithms for Baseline Worst Days 

Acadia 20% Worst 

Extinction 

(90.9 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

Great Gulf 20% Worst 

Extinction

 (87.4 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

Brigantine 20% Worst 

Extinction

 (154.0 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

Acadia 20% Worst 

Extinction

 (106.4 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSalt

Brigantine 20% Worst 

Extinction

 (194.4 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSalt

Great Gulf 20% Worst 

Extinction

 (106 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSalt
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-8

Figure 3-2. Comparison of Old and New Algorithms for Baseline Worst Days 

Moosehorn 20% Worst 

Extinction

 (79.9 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

LyeBrook 20% Worst 

Extinction

 (105.2 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

Shenandoah 20% Worst 

Extinction

 (160.6 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

LyeBrook 20% Worst 

Extinction

 (129.7 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSalt

Moosehorn 20% Worst 

Extinction

 (93.4 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSalt

Shenandoah 20% Worst 

Extinction

 (206.1 Mm-1)

ammSO4

ammNO3

OMC

EC

SOIL

Coarse

SeaSalt
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-9

3.2. 2002 Monitoring Data 

The recent MANE-VU report “2002 Year in Review” (NESCAUM, 2004b) 

provides a comprehensive review of monitoring data available to support SIP 

development in the MANE-VU region, including data on fine particle composition, as 

well as temporal and spatial distributions. The data in this study support the conceptual 

model in several important ways. They show that: (1) the single largest component of 

fine particle mass is sulfate; (2) the largest sulfate-generating emissions sources that 

affect the MANE-VU region lie to the south and west of the region; (3) fine particle 

concentrations are bi-modal with peaks in the summer and winter; and (4) summer and 

winter peak concentrations are generally caused by different chemical and physical 

processes in the atmosphere (i.e., summer peaks are strongly related to regional sulfate 

transport whereas winter peaks result from the sum of regionally-generated sulfate and 

locally generated sulfate, as well as organics and nitrate that build up during local 

stagnation events). 

3.2.1. Sulfate 

Data from several monitoring programs indicate that sulfate (on an annual basis) 

is the single largest component of fine particle mass in the MANE-VU region. Figure 3-3 

displays sample data from two Speciation Trends Network (STN) sites in New Jersey. 

This shows that sulfate accounts for roughly half of fine particle mass on an annual 

average basis at background sites and about a third at the urban site. During summer, 

sulfate comprises over half the fine particle mass at rural background sites and two-fifths 

of fine particle mass at the urban site. When considering the different light-extinguishing 

properties of various fine particle constituents, sulfate is responsible for an even greater 

fraction of visibility impairment. It accounts for between three-quarters and four-fifths of 

overall light extinction on the 20 percent worst- visibility days (Table 3-2). 

Figure 3-3. New Jersey Urban Area Compared to an Upwind Background Site 

2002 Urban-Rural PM2.5

0

5

10

15

20

25

NJ

BG

NJ

BG

NJ

BG

NJ

BG

NJ

BG

Mass (ug/m 

3) 

Other PM2.5

Soil

EC_STN

OCM_STN

AmmNO3

AmmSO4

annual winter spring summer fall
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-10

3.2.2. Southwest-Northeast Gradient 

Figure 3-4 shows that PM2.5 mass declines fairly steadily along a southwest to

northeast transect of MANE-VU. This decline is consistent with the existence of large 

fine particle emissions sources (both primary and secondary) to the south and west of the 

MANE-VU region. 

This trend in PM2.5 mass is primarily due to a marked southwest-to-northeast 

gradient in ambient sulfate concentrations during three seasons of the year as illustrated 

in Figure 3-5. Wintertime concentrations, by contrast, are far more uniform across the 

entire region. Figure 3-6 shows that on an annual basis, both total PM and sulfate mass 

are highest in the southwestern portions of MANE-VU (note the different scales for each 

pollutant). High concentrations of nitrate and organic particle constituents, which play a 

role in localized wintertime PM episodes, tend to be clustered along the northeastern 

urban corridor and in other large urban centers. 

Sulfate is a secondary pollutant, meaning that it forms in the atmosphere from 

precursor emissions. The formation of sulfate from SO2 emissions requires time in an 

oxidizing environment. Therefore, it is likely that a substantial portion of the sulfate 

observed in the MANE-VU region is from sulfur emitted from south and west of the 

region. Modeled meteorological (trajectory) data presented in Chapter 5 support this 

conclusion by showing that the dominant wind direction over the MANE-VU region 

during periods of high sulfate concentrations is from the southwest. 

Figure 3-4. MANE-VU FRM PM2.5 statistics along a southwest to northeast axis 

0

5

10

15

20

25

30

35

PA MD DE NJ NY CT MA NH VT RI ME

ug/m3

0.95%

Mean

Median

5%
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-11

Figure 3-5. 2002 Seasonal average SO4 based on IMPROVE and STN data 

Figure 3-6. 2002 Annual average PM2.5, sulfate, nitrate and total carbon for MANE-VU 

based on IMPROVE and STN data. Mass data are supplemented by the FRM network. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-12

3.2.3. Seasonality 

In general, fine particle concentrations in MANE-VU are highest during the 

warmest (summer) months but also exhibit a secondary peak during the coldest (winter) 

months. This bimodal seasonal distribution of peak values is readily apparent in Figure 

3-7. The figure shows the smoothed 60-day running average of fine particle mass 

concentrations using continuous monitoring data from two northeastern cities over a 

period of several years. 

Figure 3-7. Moving 60-day average of fine aerosol mass concentrations based on long-term 

data from two northeastern cities 

Figure 3-8. 30-day average fine aerosol mass concentrations from eight northeastern cities
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-13

Although the patterns exhibited by these monitoring data include occasional 

anomalies (as in the summer of 2000), summer peak concentrations in both cities of 

Figure 3-7 are generally much higher than the surrounding winter peaks. Figure 3-8 also 

demonstrates this bimodal pattern. Though slightly more difficult to discern in just a 

single year’s worth of data, a “W” pattern does emerge at almost all sites across the 

region during 2002 with the winter peak somewhat lower than the summer peak at most 

sites. Urban monitors in Wilmington, Delaware and New Haven, Connecticut have 

wintertime peak values approaching those of summer.

3.2.4. Seasonal Mechanisms 

In the summertime, MANE-VU sites repeatedly experience sulfate events due to 

transport from regions to the south and west. During such events, rural and urban sites 

throughout the MANE-VU region record high (i.e., >15 μg/m3

) daily average PM2.5

concentrations. Meteorological conditions during the summer frequently allow for 

summer “stagnation” events when very low wind speeds and warm temperatures allow 

pollution levels to build in an air mass as it is slowly transported across the continent. 

During these events, atmospheric ventilation is poor and local emission sources add to the 

burden of transported pollution with the result that concentrations throughout the region 

(both rural and urban) are relatively uniform. Generally there are enough of these events 

to drive the difference between urban and rural sites down to less than 1 μg/m3 during the 

warm or hot months of the year. As a result, concentrations of fine particles aloft will 

often be higher than at ground-level during the summertime, especially at rural 

monitoring sites. Thus, when atmospheric “mixing” occurs during summer13 mornings 

(primarily 7 to 11 a.m.), fine particle concentrations at ground-level can actually increase 

(see Hartford, CT or Camden, NJ in Figure 3-9). 

During the wintertime, strong inversions frequently trap local emissions overnight 

and during the early morning, resulting in elevated urban concentrations. These 

inversions occur when the earth’s surface loses thermal energy by radiating it into the 

atmosphere (especially on clear nights). The result is a cold, stable layer of air near the 

ground. At sunrise, local emissions (both mobile and stationary) begin increasing in 

strength and build-up in the stable ground layer (which may extend only 100 meters or 

less above-ground). Increasing solar radiation during the period between 10 a.m. and 

noon typically breaks this cycle by warming the ground layer so that it can rise and mix 

with air aloft. Because the air aloft during wintertime is typically less polluted than the 

surface layer, this mixing tends to reduce ground-level particle concentrations (see Figure 

3-10). This diurnal cycle generally drives wintertime particle concentrations, although 

the occasional persistent temperature inversion can have the effect of trapping and 

concentrating local emissions over a period of several days, thereby producing a 

significant wintertime pollution episode. 

 

13 Here we define summer as May, June, July and August. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-14

Figure 3-9. Mean hourly fine aerosol concentrations during the summer season 

Fine Aerosol Diurnal Pattern at 18 MANE-VU Sites

0

5

10

15

20

25

30

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

ug/m3

Hartford, CT New Haven, CT Waterbury, CT Wilmington, DE

Lewiston, ME Portland, ME Bangor, ME Camden, NJ

Newark, NJ Trenton, NJ Elizabeth, NJ NY City, NY

Buffalo, NY Rochester, NY Pittsburgh, PA Burlington, VT

Rutland, VT Boston, MA

Figure 3-10. Mean hourly fine aerosol concentrations during the winter season 

Fine Aerosol Diurnal Pattern at 18 MANE-VU Sites

0

5

10

15

20

25

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

ug/m3

Hartford, CT New Haven, CT Waterbury, CT Wilmington, DE

Lewiston, ME Portland, ME Bangor, ME Camden, NJ

Newark, NJ Trenton, NJ Elizabeth, NJ NY City, NY

Buffalo, NY Rochester, NY Pittsburgh, PA Burlington, VT

Rutland, VT Boston, MA
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-15

Rural areas experience the same temperature inversions but have relatively fewer 

local emissions sources so that wintertime concentrations in rural locations tend to be 

lower than those in nearby urban areas. Medium and long-range fine particle transport 

events do occur during the winter but to a far lesser extent than in the summertime. In 

sum, it is the interplay between local and distant sources together with seasonal 

meteorological conditions that drives the observed 3–4 μg/m3

 wintertime rural versus 

urban difference in PM concentrations. 

3.3. RAIN data 

Routine monitoring networks operated by USEPA, the National Park Service or 

state monitoring agencies collected much of the monitoring data shown so far. We 

anticipate that these data will continue to provide crucial information on the nature and 

extent of visibility impairment across the region. In addition, MANE-VU is also 

developing a network of enhanced monitoring sites capable of providing continuous data 

on the concentration, composition, and visibility impacts of fine particles. These data 

will be critical for understanding the more complex issues associated with organic carbon 

as well as any tradeoffs between sulfate and nitrate control. This Rural Aerosol Intensive 

Network (RAIN), which was first deployed in 2004, is therefore likely to play a 

prominent role in future visibility control programs and in the development of regional 

haze SIPs due in 2018. 

NESCAUM coordinates the RAIN effort as a cooperative effort of the MANEVU member state air agencies. The network covers the region from western Maryland 

(near large sulfur sources in the Ohio River Valley) through northwestern Connecticut to 

Acadia National Park in Maine. The initial network consists of these three rural, 

moderate elevation (700 to 2,500 feet) sites in a southwest to northeast line, all with 

detailed PM and visibility related measurements. The network design includes highly 

time resolved (1-2 hour) aerosol mass, composition, and optical property measurements. 

These provide enhanced insight into regional aerosol generation and source 

characterization, which are factors that drive short term visibility, and aerosol model 

performance and evaluation. In addition to these three sites, as of 2006 the NYDEC/SUNY-Albany intensive measurement site at Pinnacle State Park (Addison, NY, 

seven miles southwest of Corning, NY, and seven miles north of the Pennsylvania 

border) has most of the RAIN parameters and methods other than visibility; efforts are 

underway to bring that site into the RAIN program (to ensure consistent method 

operation) and to add visibility measurements. 

The RAIN sites use the Sunset Laboratory Model 3 field carbon analyzer and the 

new Thermo Environmental Model 5020 sulfate analyzer. This is the first use of these 

methods in routine, ongoing state-run networks. Combined with other more routine 

measurements such as IMPROVE aerosol, NGN-2 (wet) nephelometers, continuous 

PM2.5, trace SO2, ozone, meteorology, and automated digital visibility cameras 

(CAMNET), these methods make up the core RAIN monitoring lineup. Some of the 

RAIN sites will have additional related measurements, including “true” trace CO, NOX, 

dry scattering (NGN-3a nephelometer), and other measurements. An Air and Waste 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-16

Management Association conference proceedings paper provides more information on 

the design of the network and examples of data from the summer of 2004.14 

A longer term goal of RAIN is to enhance the network with other measurements 

and sites in future years. A National Weather Service ASOS visibility sensor at a RAIN 

site would allow the large network of existing ASOS data to be “tethered” to visibility 

measurements we understand well. Strong aerosol acidity, nitric acid, and ammonia are 

measurements that would be desirable on either an integrated or real-time basis. There 

are no continuous nitrate measurements in RAIN at this time because available methods 

suitable for routine deployment in state networks are not yet sufficiently robust.15 Lack 

of continuous nitrate data is not a significant issue for this analysis since nitrate is not 

(yet) a major visibility factor at these rural sites. We expect that most of the continuous 

method data from RAIN to be available in real-time to web data resources like VIEWS, 

FASTNET and AIRNowTech by the end of 2006. 

Measurements similar to those in RAIN done towards the west and south borders 

of the MANE-VU domain (Ohio and Virginia for example) would greatly enhance our 

understanding of the impact of the large sulfur source region in and around the Ohio 

River Valley on regional visibility. We encourage agencies and RPOs in those areas to 

develop intensive sites to complement the RAIN data. 

As an initial test of the RAIN network, we examined visibility and related particle 

information for the third quarter of 2004 to determine how well the data from one (or 

both) of two recently installed semi-continuous monitors could reproduce the visibility 

data reported by existing NGN-2a nephelometers. The relevant data came from two 

monitors of interest: the Thermo Model 5020 (for sulfate) and the Sunset Labs (Model 3) 

semi-continuous analyzer for elemental and organic carbon. In addition, a Rotronic 

sensor (Model MP-101A, with active aspiration) measured relative humidity (RH) data 

on-site in order to supply a correction factor - f(RH) - for estimating the light scattering 

associated with various fine particle constituents.

Because ammonium sulfate is the major component of haze-producing particulate 

pollution in the northeastern United States, we examined sulfate data first. The Thermo 

Model 5020 reports sulfate and the IMPROVE algorithm for calculating visibility 

parameters assumes that all sulfate is in the form of ammonium sulfate. During high 

sulfate events in the rural Northeast this is not always the case, although it is still a 

reasonable first assumption. 

The Thermo sulfate method has been shown to consistently under-report sulfate 

relative to IMPROVE sulfate measurements at the RAIN sites, but not at some other 

sites. Since the correlation with IMPROVE sulfate is high at all RAIN sites, the hourly 

RAIN sulfate data can be corrected to be “IMPROVE”-like with reasonable confidence. 

A RAIN technical memorandum describes this issue in more detail.16 For the Acadia 

sulfate data used here, the daily correlation coefficient (R2

) between IMPROVE and 

 

14 http://www.nescaum.org/documents/allen-awma_haze-rain-paper-oct-2004_proceedings.pdf/ 

15 See the EPA method evaluation report at http://www.epa.gov/ttn/amtic/semicontin.html for more 

information. 

16 “Rural Aerosol Intensive Network (RAIN) Preliminary Data Analysis,” available at: 

http://www.nescaum.org/documents/2006-05-memo8-rain.pdf/
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-17

Thermo sulfate is 0.95 (based on third and fourth quarter 2004 data). A correction factor 

of 1.30 is applied to the Thermo sulfate data based on the linear regression of IMPROVE 

and Thermo sulfate 24-hour samples for the third and fourth quarters of 2004 data; this 

correction makes the Thermo sulfate data consistent with the IMPROVE sulfate data. 

We need three types of data to relate direct measures of atmospheric light 

scattering to a re-constructed or calculated estimate of light scattering based on observed 

sulfate levels: (1) direct measurements of light scattering (via nephelometer); (2) sulfate 

measurements; and (3) relative humidity measurements. The three RAIN sites in the 

northeastern United States measure each of these variables. Of these sites, however, only 

the McFarland Hill site at Acadia National Park in Maine is within a Class I area. 

Therefore, we selected data from the McFarland Hill site for the preliminary analysis we 

describe below. 

Given the highly non-linear relationship between relative humidity and 

ammonium sulfate particle size and the limitations of relative humidity (RH) sensor 

accuracy at very high values of RH, we excluded from this analysis data collected when 

relative humidity was equal to or greater than 95 percent. Of the 2,208 hourly 

observations recorded from June 1 through September 30, this relative humidity 

‘exclusion’ removed 525 hours. Data for an additional 92 hours were not available due to 

missing measurements from either the sulfate monitor or the nephelometer. We excluded 

a further 35 hours due to flagged nephelometer performance (such flags could be 

triggered by excess noise or rate-of-change in the signal). This left 1,556 hourly 

observation pairs for the third quarter, equivalent to a data capture rate of 70 percent - 

still a substantial sample given the nature of the emerging technology employed at the 

RAIN sites. 

We multiplied sulfate concentrations from the Thermo 5020 by 1.37 to convert 

them to a mass equivalent for ammonium sulfate (this is the same factor IMPROVE 

uses). This new variable (SULFATE) is the strongest driver of light extinction in the 

Northeast because of the extreme size-dependent nature of ammonium sulfate light 

scattering, which in turn is highly (and very non-linearly) dependent on atmospheric 

relative humidity. Next, we converted the hourly RH values to a relative humidity 

function “f(RH)” by using a conversion table adopted by IMPROVE.17 Then we applied 

a “dry specific scattering” coefficient of “3”18 to the hourly SULFATE values. The final 

equation is shown below: 

Reconstructed Sulfate Scattering = 3 * f(RH) * (SULFATE) 

When we compared this reconstructed estimate of hourly light scattering to the 

IMPROVE NGN-2a nephelometer data (via a least-squares linear regression), we 

obtained an R2

 of 0.888. When two apparent outlier hours are removed (both of which 

occurred during periods when relative humidity was over 87 percent and changing 

rapidly) the regression slope is 0.846, the intercept is -5, and R2

 increases to 0.942. This 

 

17 See: http://vista.cira.colostate.edu/improve/Tools/humidity_correction.htm; this is the original f(RH) 

table, not the new one. 

18 Described at http://vista.cira.colostate.edu/improve/Tools/ReconBext/reconBext.htm
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-18

implies that sulfate alone is responsible for approximately 85 percent of the light 

scattering (and visibility degradation) for this period of measurement. 

Because elemental carbon absorbs light much more strongly than it scatters light, 

we added only the “light-scattering carbon” (OC) detected by the Sunset Model 3 to this 

reconstruction. The IMPROVE program uses the following equation to describe the 

impact of light-scattering carbon: 

Reconstructed Carbon Scattering = 4 * forg(RH) * [OMC] 

where the dry scattering coefficient of this carbon fraction is set at “4,” the relative 

humidity factor is set at unity (due to the weak hygroscopicity of organic carbon), and 

OMC represents “organic mass by carbon.” The IMPROVE Steering Committee has 

recently adopted 1.8 as an alternative organic mass multiplier (rather than 1.4) for 

calculating OMC values for use in reconstructed extinction as described in section 3.1. 

We have also used 1.8 for the analysis presented below. 

Because the RAIN sites collect carbon data over two-hour periods, we averaged 

the McFarland Hill sulfate (Thermo-5020), scattering (NGN-2) and RH (Rotronic) hourly 

data into two-hour, whole number blocks in order to bring the data from Sunset Labs into 

the reconstruction equation. In addition, we subtracted a “filter blank” value for the 

Sunset OC data of 0.5 μg/m3

 (empirically derived from user experience of the Model 3) 

from the OC data prior to their use in the reconstruction calculation (OMC = (Sunset OC 

– 0.5) x 1.8). See Figure 3-11 for results of these reconstructed estimates of visibility 

using both sulfate and carbon measurements. 

As indicated by Figure 3-11, adding the organic carbon data to the sulfate data 

significantly improves the agreement between reconstructed estimates of aerosol 

scattering and direct visibility measurements at the McFarland Hill site. Specifically, it 

appears that these two components of the ambient aerosol generally explain about 94 

percent of the observed scattering at Acadia during the summer, with a very high 

correlation coefficient even at 2-hour intervals. This is excellent agreement considering 

that scattering from nitrate and crustal aerosol components is not included in this 

reconstruction. 

These data demonstrate that the highly time-resolved nature of RAIN data is 

invaluable in examining short-term variations (i.e., on the order of days to weeks) in haze 

production and transport. The sulfate, carbon and other monitoring capabilities emerging 

from the RAIN project will provide another valuable tool to state and tribal authorities in 

seeking to understand the sources of regional haze and to craft effective control 

strategies. A more detailed analysis of RAIN data is available in a recently released 

MANE-VU technical memorandum.19

 

19 “Rural Aerosol Intensive Network (RAIN) Preliminary Data Analysis,” available at: 

http://www.nescaum.org/documents/2006-05-memo8-rain.pdf/
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-19

Figure 3-11. 2-Hour Reconstructed scattering at Acadia, Maine using semicontinuous SO4 and OC data for the third quarter of 2004 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 3-20

References 

NESCAUM, “Natural Background Visibility Conditions: Considerations and Proposed 

Approach to the Calculation of Natural Background Visibility Conditions at MANE-VU 

Class I Areas,” Northeast States for Coordinated Air Use Management, Boston, MA, 

June 2004a. 

NESCAUM, “2002: A Year in Review,” Northeast States for Coordinated Air Use 

Management, Boston, MA, December, 2004b. 

USEPA, “Guidance for Tracking Progress under the Regional Haze Rule,” EPA-454/B03-004 September 2003a. 

USEPA, “Guidance for Estimating Natural Visibility Conditions under the Regional 

Haze Program,” EPA-454/B-03-005 September 2003b. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-1

4. HAZE-ASSOCIATED POLLUTANT EMISSIONS 

This chapter explores the origin and quantity of haze-forming pollutants emitted 

in the eastern and the mid-Atlantic United States. It also describes the procedures used to 

prepare emissions inventory data for use in chemical transport models (Chapter 6 

describes in greater detail the models themselves). 

The pollutants that affect fine particle formation, and thus contribute to regional 

haze, are sulfur oxides (SOX), nitrogen oxides (NOX), volatile organic compounds 

(VOC), ammonia (NH3), and particles with an aerodynamic diameter less than or equal to 

10 and 2.5 μm (i.e., primary PM10 and PM2.5). The emissions dataset illustrated below is 

the 2002 MANE-VU Version 2 regional haze emissions inventory. The emission 

inventories include carbon monoxide (CO), but we do not consider that pollutant here as 

it does not contribute to regional haze. The MANE-VU regional haze emissions 

inventory version 3.0, released in April 2006, has superseded version 2 for modeling 

purposes. This inventory update was developed through the Mid-Atlantic Regional Air 

Management Association (MARAMA) for the MANE-VU RPO. The comparative 

observations among recent emission inventories presented here (the 1996 USEPA NET 

and 1999 NEI) would hold true were version 3.0 substituted for version 2.0. 20

 

The first section of this chapter describes emission characteristics by pollutant and 

source type (e.g., point, area, and mobile). The second section describes on-going efforts 

to process emissions inventory data in support of air quality modeling. The final section 

provides source apportionment estimates for several MANE-VU Class 1 areas based on 

2002 SO2 inventory data. 

4.1. Emissions Inventory Characteristics 

4.1.1. Sulfur Dioxide (SO2) 

SO2 is the primary precursor pollutant for sulfate particles. Sulfate particles 

commonly account for more than 50 percent of particle-related light extinction at 

northeastern Class I areas on the clearest days and for as much as or more than 80 percent 

 

20 EPA's Emission Factor and Inventory Group (EFIG) (USEPA/OAR (Office of Air and 

Radiation)/OAQPS (Office of Air Quality Planning and Standards)/EMAD (Emissions, Monitoring and 

Analysis Division) prepares a national database of air emissions information with input from numerous 

state and local air agencies, from tribes, and from industry. This database contains information on 

stationary and mobile sources that emit criteria air pollutants and their precursors, as well as hazardous air 

pollutants (HAPs). The database includes estimates of annual emissions, by source, of air pollutants in 

each area of the country on an annual basis. The NEI includes emission estimates for all 50 states, the 

District of Columbia, Puerto Rico, and the Virgin Islands. Emission estimates for individual point or major 

sources (facilities), as well as county level estimates for area, mobile and other sources, are available 

currently for years 1985 through 1999 for criteria pollutants, and for years 1996 and 1999 for HAPs. Data 

from the NEI help support air dispersion modeling, regional strategy development, setting regulation, air 

toxics risk assessment, and tracking trends in emissions over time. For emission inventories prior to 1999, 

the National Emission Trends (NET) database maintained criteria pollutant emission estimates and the 

National Toxics Inventory (NTI) database maintained HAP emission estimates. Beginning with 1999, the 

NEI began preparing criteria and HAP emissions data in a more integrated fashion to take the place of the 

NET and the NTI. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-2

on the haziest days. Hence, SO2 emissions are an obvious target of opportunity for

reducing regional haze in the eastern United States. Combustion of coal and, to a 

substantially lesser extent, of certain petroleum products accounts for most anthropogenic 

SO2 emissions. In fact, in 1998 a single source category — coal-burning power plants — 

was responsible for two-thirds of total SO2 emissions nationwide (NESCAUM, 2001a).

Figure 4-1 shows SO2 emissions trends in the MANE-VU states extracted from 

the NEI for the years 1996, 1999, and the 2002 MANE-VU inventory (USEPA, 2005; 

MARAMA, 2004). Most of the states (with the exception of Maryland) show declines in 

year 2002 annual SO2 emissions as compared to 1996 emissions. Some of the states show 

an increase in 1999 followed by a decline in 2002 and others show consistent declines 

throughout the entire period. The upward trend in emissions after 1996 probably reflects 

electricity demand growth during the late 1990s combined with the availability of banked 

emissions allowances from initial over-compliance with control requirements in Phase 1 

of the USEPA Acid Rain Program. This led to relatively low market prices for 

allowances later in the decade, which encouraged utilities to purchase allowances rather 

than implement new controls as electricity output expanded. The observed decline in the 

2002 SO2 emissions inventory reflects implementation of the second phase of the USEPA 

Acid Rain Program, which in 2000 further reduced allowable emissions and extended 

emissions limits to more power plants. Figure 4-2 shows the percent contribution from 

different source categories to overall, annual 2002 SO2 emissions in the MANE-VU 

states. The chart shows that point sources dominate SO2 emissions, which primarily 

consist of stationary combustion sources for generating electricity, industrial energy, and 

heat. Smaller stationary combustion sources called “area sources” (primarily commercial 

and residential heating) are another important source category in the MANE-VU states. 

By contrast, on-road and non-road mobile sources make only a relatively small 

contribution to overall SO2 emissions in the region (NESCAUM, 2001a). 

4.1.2. Volatile Organic Compounds (VOC) 

Existing emission inventories generally refer to “volatile organic compounds” 

(VOCs) for hydrocarbons whose volatility in the atmosphere makes them particularly 

important from the standpoint of ozone formation. From a regional haze perspective, we 

are concerned less with the volatile organic gases emitted directly to the atmosphere and 

more with the secondary organic aerosol (SOA) that the VOCs form after condensation 

and oxidation processes. Thus the VOC inventory category is of interest primarily from 

the organic carbon perspective of PM2.5. After sulfate, organic carbon generally accounts

for the next largest share of fine particle mass and particle-related light extinction at 

northeastern Class I sites. The term organic carbon encompasses a large number and 

variety of chemical compounds that may come directly from emission sources as a part of 

primary PM or may form in the atmosphere as secondary pollutants. The organic carbon 

present at Class I sites almost certainly includes a mix of species, including pollutants 

originating from anthropogenic (i.e., manmade) sources as well as biogenic hydrocarbons 

emitted by vegetation. Recent efforts to reduce manmade organic carbon emissions have 

been undertaken primarily to address summertime ozone formation in urban centers. 

Future efforts to further reduce organic carbon emissions may be driven by programs that 

address fine particles and visibility. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-3

Figure 4-1. State Level Sulfur Dioxide Emissions 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

million t/y

NEI 1996

NEI 1999

MV 2002 v.2

Figure 4-2. SO2 (Bar graph: Percentage fraction of four source categories, Circle: 

Annual emissions amount in 106 tons per year) 

0%

25%

50%

75%

100%

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

0.0

0.2

0.4

0.6

0.8

1.0

1.2

million t/y

AREA NONROAD ONROAD POINT Emission
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-4

Understanding the transport dynamics and source regions for organic carbon in 

northeastern Class I areas is likely to be more complex than for sulfate. This is partly 

because of the large number and variety of OC species, the fact that their transport 

characteristics vary widely, and the fact that a given species may undergo numerous 

complex chemical reactions in the atmosphere. Thus, the organic carbon contribution to 

visibility impairment at most Class I sites in the East is likely to include manmade 

pollution transported from a distance, manmade pollution from nearby sources, and 

biogenic emissions, especially terpenes from coniferous forests. 

As shown in Figure 4-3, the VOC inventory is dominated by mobile and area 

sources. On-road mobile sources of VOCs include exhaust emissions from gasoline 

passenger vehicles and diesel-powered heavy-duty vehicles as well as evaporative 

emissions from transportation fuels. VOC emissions may also originate from a variety of 

area sources (including solvents, architectural coatings, and dry cleaners) as well as from 

some point sources (e.g., industrial facilities and petroleum refineries). 

Biogenic VOCs may play an important role within the rural settings typical of 

Class I sites. The oxidation of hydrocarbon molecules containing seven or more carbon 

atoms is generally the most significant pathway for the formation of light-scattering 

organic aerosol particles (Odum et al., 1997). Smaller reactive hydrocarbons that may 

contribute significantly to urban smog (ozone) are less likely to play a role in organic 

aerosol formation, though we note that high ozone levels can have an indirect effect on 

visibility by promoting the oxidation of other available hydrocarbons, including biogenic 

Figure 4-3. VOC (Bar graph: Percentage fraction of four source categories, Circle: 

Annual emissions amount in 106 tons per year) 

0%

25%

50%

75%

100%

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

million t/y

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

AREA NONROAD ONROAD POINT Emission
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-5

emissions (NESCAUM, January 2001). In short, we need further work to characterize 

the organic carbon contribution to regional haze in the Northeast and Mid-Atlantic states 

and to develop emissions inventories that will be of greater value for visibility planning 

purposes. 

4.1.3. Oxides of Nitrogen (NOX) 

NOX emissions contribute directly to visibility impairment in the eastern U.S. by 

forming light-scattering nitrate particles. Nitrate generally accounts for a substantially 

smaller fraction of fine particle mass and related light extinction than sulfate and organic 

carbon at northeastern Class I sites. Notably, nitrate may play a more important role at 

urban sites and in the wintertime. In addition, NOX may have an indirect effect on 

summertime visibility by virtue of its role in the formation of ozone, which in turn 

promotes the formation of secondary organic aerosols (NESCAUM 2001a). 

Figure 4-4 shows NOX emissions in the MANE-VU region at the state level. 

Since 1980, nationwide emissions of NOX from all sources have shown little change. In 

fact, emissions increased by 2 percent between 1989 and 1998 (USEPA, 2000a). This 

increase is most likely due to industrial sources and the transportation sector, as power 

plant combustion sources have implemented modest emissions reductions during the 

same time period. Most states in the MANE-VU region experienced declining NOX

emissions from 1996 through 2002, except Massachusetts, Maryland, New York, and 

Rhode Island, which show an increase in NOX emissions in 1999 before declining to 

levels below 1996 emissions in 2002. 

Power plants and mobile sources generally dominate state and national NOX

emissions inventories. Nationally, power plants account for more than one-quarter of all 

NOX emissions, amounting to over six million tons. The electric sector plays an even 

larger role, however, in parts of the industrial Midwest where high NOX emissions have a 

particularly significant power plant contribution. By contrast, mobile sources dominate 

the NOX inventories for more urbanized Mid-Atlantic and New England states to a far 

greater extent, as shown in Figure 4-5. In these states, on-road mobile sources — a 

category that mainly includes highway vehicles — represent the most significant NOX

source category. Emissions from non-road (i.e., off-highway) mobile sources, primarily 

diesel-fired engines, also represent a substantial fraction of the inventory. While there are 

fewer uncertainties associated with available NOX estimates than in the case of other key 

haze-related pollutants — including primary fine particle and ammonia emissions — 

further efforts could improve current inventories in a number of areas (NESCAUM, 

2001a). 

In particular, better information on the contribution of area and non-highway 

mobile sources may be of most interest in the context of regional haze planning. First, 

available emission estimation methodologies are weaker for these types of sources than 

for the large stationary combustion sources. Moreover, because SO2 and NOX emissions 

must mix with ammonia to participate in secondary particle formation, emissions that 

occur over large areas at the surface may be more efficient in secondary fine particulate 

formation than concentrated emissions from isolated tall stacks (Duyzer, 1994). 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-6

Figure 4-4. State Level Nitrogen Oxides Emissions 

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

million t/y

NEI 1996

NEI 1999

MV 2002 v.2

Figure 4-5. NOX (Bar graph: Percentage fraction of four source categories, Circle: 

Annual emissions amount in 106 tons per year) 

0%

25%

50%

75%

100%

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

million t/y

0.0

0.3

0.6

0.9

AREA NONROAD ONROAD POINT Emission
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-7

4.1.4. Primary Particulate Matter (PM10 and PM2.5) 

Directly-emitted or “primary” particles (as distinct from secondary particles that 

form in the atmosphere through chemical reactions involving precursor pollutants like 

SO2 and NOX) can also contribute to regional haze. For regulatory purposes, we make a 

distinction between particles with an aerodynamic diameter less than or equal to 10 

micrometers and smaller particles with an aerodynamic diameter less than or equal to 2.5 

micrometers (i.e., primary PM10 and PM2.5, respectively). 

Figure 4-6 and Figure 4-7 show PM10 and PM2.5 emissions for the MANE-VU 

states for the years 1996, 1999, and 2002. Note that for PM10 the inventory values are 

drawn from the 2002 NEI. Most states show a steady decline in annual PM10 emissions 

over this time period. By contrast, emission trends for primary PM2.5 are more variable. 

Crustal sources are significant contributors of primary PM emissions. This 

category includes fugitive dust emissions from construction activities, paved and unpaved 

roads, and agricultural tilling. Typically, monitors estimate PM10 emissions from these 

types of sources by measuring the horizontal flux of particulate mass at a fixed downwind 

sampling location within perhaps 10 meters of a road or field. Comparisons between 

estimated emission rates for fine particles using these types of measurement techniques 

and observed concentrations of crustal matter in the ambient air at downwind receptor 

sites suggest that physical or chemical processes remove a significant fraction of crustal 

material relatively quickly. As a result, it rarely entrains into layers of the atmosphere 

where it can transport to downwind receptor locations. Because of this discrepancy 

between estimated emissions and observed ambient concentrations, modelers typically 

reduce estimates of total PM2.5 emissions from all crustal sources by applying a factor of 

0.15 to 0.25 before including in modeling analyses.

From a regional haze perspective, crustal material generally does not play a major 

role. On the 20 percent best-visibility days during the baseline period (2000-2004), it 

accounted for six to eleven percent of particle-related light extinction at MANE-VU 

Class 1 sites. On the 20 percent worst-visibility days, however, crustal material generally 

plays a much smaller role relative to other haze-forming pollutants, ranging from two to 

three percent. Moreover, the crustal fraction includes material of natural origin (such as 

soil or sea salt) that is not targeted under the Haze Rule. Of course, the crustal fraction 

can be influenced by certain human activities, such as construction, agricultural practices, 

and road maintenance (including wintertime salting) — thus, to the extent that these types 

of activities are found to affect visibility at northeastern Class I sites, control measures 

targeted at crustal material may prove beneficial. 

Experience from the western United States, where the crustal component has 

generally played a more significant role in driving overall particulate levels, may be 

helpful to the extent that it is relevant in the eastern context. In addition, a few areas in 

the Northeast, such as New Haven, Connecticut and Presque Isle, Maine, have some 

experience with the control of dust and road-salt as a result of regulatory obligations 

stemming from their past non-attainment status with respect to the NAAQS for PM10. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-8

Figure 4-6. State Level Primary PM10 Emissions 

0.0

0.2

0.4

0.6

0.8

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

million t/y

NEI 1996

NEI 1999

NEI 2002

Figure 4-7. State Level Primary PM2.5 Emissions 

0.0

0.1

0.2

0.3

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

million t/y

NEI 1996

NEI 1999

MV 2002 v.2
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-9

Current emissions inventories for the entire MANE-VU area indicate residential 

wood combustion represents 25 percent of primary fine particulate emissions in the 

region. This implies that rural sources can play an important role in addition to the 

contribution from the region’s many highly populated urban areas. An important 

consideration in this regard is that residential wood combustion occurs primarily in the 

winter months, while managed or prescribed burning activities occur largely in other 

seasons. The latter category includes agricultural field-burning activities, prescribed 

burning of forested areas and other burning activities such as construction waste burning. 

Limiting burning to times when favorable meteorological conditions can efficiently 

disperse resulting emissions can manage many of these types of sources. 

Figure 4-8 and Figure 4-9 show that area and mobile sources dominate primary 

PM emissions. (The NEI inventory categorizes residential wood combustion and some 

other combustion sources as area sources.) The relative contribution of point sources is 

larger in the primary PM2.5 inventory than in the primary PM10 inventory since the crustal 

component (which consists mainly of larger or “coarse-mode” particles) contributes 

mostly to overall PM10 levels. At the same time, pollution control equipment commonly 

installed at large point sources is usually more efficient at capturing coarse-mode 

particles. 

4.1.5. Ammonia Emissions (NH3) 

Knowledge of ammonia emission sources will be necessary in developing 

effective regional haze reduction strategies because of the importance of ammonium 

sulfate and ammonium nitrate in determining overall fine particle mass and light 

scattering. According to 1998 estimates, livestock agriculture and fertilizer use 

accounted for approximately 86 percent of all ammonia emissions to the atmosphere 

(USEPA, 2000b). We need, however, better ammonia inventory data for the 

photochemical models used to simulate fine particle formation and transport in the 

eastern United States. Because the USEPA does not regulate ammonia as a criteria 

pollutant or as a criteria pollutant precursor, these data do not presently exist at the same 

level of detail or certainty as for NOX and SO2. 

Ammonium ion (formed from ammonia emissions to the atmosphere) is an 

important constituent of airborne particulate matter, typically accounting for 10–20 

percent of total fine particle mass. Reductions in ammonium ion concentrations can be 

extremely beneficial because a more-than-proportional reduction in fine particle mass can 

result. Ansari and Pandis (1998) showed that a one μg/m3 reduction in ammonium ion 

could result in up to a four μg/m3 reduction in fine particulate matter. Decision makers, 

however, must weigh the benefits of ammonia reduction against the significant role it 

plays in neutralizing acidic aerosol.21

 

21 SO2 reacts in the atmosphere to form sulfuric acid (H2SO4). Ammonia can partially or fully neutralize 

this strong acid to form ammonium bisulfate or ammonium sulfate. If planners focus future control 

strategies on ammonia and do not achieve corresponding SO2 reductions, fine particles formed in the 

atmosphere will be substantially more acidic than those presently observed. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-10

Figure 4-8. Primary PM10 (Bar graph: Percentage fraction of four source categories, 

Circle: Annual emissions amount in 106 tons per year) 

0%

20%

40%

60%

80%

100%

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

million t/y

0.0

0.1

0.2

0.3

0.4

0.5

AREA NONROAD ONROAD POINT Emission

Figure 4-9. Primary PM2.5 (Bar graph: Percentage fraction of four source 

categories, Circle: Annual emissions amount in 106 tons per year) 

0%

25%

50%

75%

100%

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

million t/y

0.00

0.05

0.10

0.15

0.20

AREA NONROAD ONROAD POINT Emission
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-11

To address the need for improved ammonia inventories, MARAMA, NESCAUM 

and USEPA funded researchers at Carnegie Mellon University (CMU) in Pittsburgh to 

develop a regional ammonia inventory (Davidson et al., 1999). This study focused on 

three issues with respect to current emissions estimates: (1) a wide range of ammonia 

emission factor values, (2) inadequate temporal and spatial resolution of ammonia 

emissions estimates, and (3) a lack of standardized ammonia source categories. 

The CMU project established an inventory framework with source categories, 

emissions factors, and activity data that are readily accessible to the user. With this 

framework, users can obtain data in a variety of formats22 and can make updates easily, 

allowing additional ammonia sources to be added or emissions factors to be replaced as 

better information becomes available (Strader et al., 2000; NESCAUM, 2001b). 

Figure 4-10 shows that estimated ammonia emissions were fairly stable in the 

1996, 1999, and 2002 NEI for MANE-VU states, with some increases observed for 

Massachusetts, New Jersey and New York. Area and on-road mobile sources dominate 

the ammonia inventory, according to Figure 4-11. Specifically, emissions from 

agricultural sources and livestock production account for the largest share of estimated 

ammonia emissions in the MANE-VU region, except in the District of Columbia. The 

two remaining sources with a significant emissions contribution are wastewater treatment 

systems and gasoline exhaust from highway vehicles. 

Figure 4-10. State Level Ammonia Emissions 

0.000

0.025

0.050

0.075

0.100

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

million t/y

NEI 1996

NEI 1999

MV 2002 v.2

 

22 For example, the user will have the flexibility to choose the temporal resolution of the output emissions 

data or to spatially attribute emissions based on land-use data. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-12

Figure 4-11. NH3 (Bar graph: Percentage fraction of four source categories, Circle: 

Annual emissions amount in 106 tons per year) 

0%

25%

50%

75%

100%

Connecticut

Delaware

District of Columbia

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

million t/y

0.00

0.06

0.12

AREA NONROAD ONROAD POINT Emission

4.2. Contribution Assessments Based on Emissions Inventories 

Two data analysis methods have been developed that directly combine emission 

inventory data with meteorological data in order to provide first-order contributions to 

observed sulfate from individual states. The first approach, known as “Q/d,” evaluates 

the state contribution as a proportion of the ratio of the total SO2 emissions from that state 

and the distance from the state to the receptor. States and sources are assigned wind 

sectors to account for prevailing wind patterns in establishing contributions. The second 

approach, known as “Emissions times Upwind Probability,” evaluates the state 

contribution through the use of ensemble back trajectories (See Appendix A for a more 

detailed description of trajectory methods). The back trajectory-derived residence times 

of air parcels have been mapped onto a grid to create a “residence time probability field,” 

which is then multiplied by an SO2 emissions field to obtain estimated source 

contributions. The results of the two approaches are compared for receptor sites in and 

around the MANE-VU region. 

4.2.1. Sulfur Dioxide Emissions Divided by Distance 

Aggregated over long periods of time and large geographic areas, the total 

atmospheric sulfate contribution from a specific source, state, or region should be 

approximately proportionate to its SO2 emissions. For specific receptor locations, like a

Class 1 visibility area, relative impacts decrease with increasing distance from the source. 

Impacts diminish over distance as pollutants are dispersed in the atmosphere and 

removed through deposition. For non-reactive primary pollutant emissions, the 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-13

relationship between atmospheric concentrations and distance (d) can be approximated as 

a function of 1/d2. For secondary pollutants like sulfate, reductions in ambient 

concentrations that occur as a result of dispersion and deposition mechanisms are 

partially offset by the formation of secondary aerosol such that an increasing fraction of 

the remaining downwind sulfur is converted to aerosol sulfate. In these cases, the effects 

of distance are better characterized by the function 1/d. During regional sulfate episodes 

when sulfur conversion rates are enhanced by the presence of gas and aqueous-phase 

oxidants, pollutant concentrations decline even less rapidly with distance as accelerated 

aerosol formation rates work to both generate more sulfate and reduce the remaining 

sulfur available for deposition (deposition rates are roughly an order of magnitude slower 

for sulfate than for SO2). 

One simple technique for deducing the relative impact of emissions from specific 

point sources on a specific receptor site involves calculating the ratio of annual emissions 

(Q) to source-receptor distance (d).23 This empirical relationship is reasonable based on 

simple dispersion assumptions. Results from SO2 modeling using the CALPUFF 

(California Puff) model (EarthTech, 2004) further bolster its validity by showing a strong 

relationship between emissions and distance. In fact, this extremely simple method of 

estimating impact can be significantly improved to account for some aspects of 

meteorology by scaling results according to the extremely linear relationships between 

CALPUFF and Q/d values within specific wind sectors. 

The geographic domain of the sources included in the Q/d study consisted of U.S. 

states in the CENRAP, MANE-VU, VISTAS, and MIDWEST RPO regions. Canadian 

provinces in the lower eastern region were also included. The categories of SO2 emission 

sources included in this analysis were area sources (e.g., residential boilers and heaters), 

non-road mobile sources (e.g., tractors and construction vehicles), and point sources (e.g., 

industrial smokestacks and power generation facilities).24 Results were calculated for 

seven receptors including: Acadia National Park, Brigantine Wilderness in the Forsythe 

Wildlife Preserve, Dolly Sods Wilderness, Lye Brook Wilderness, Moosehorn 

Wilderness, Presidential Range-Dry River Wilderness, and Shenandoah National Park. 

The empirical formula that relates emission source strength and estimated impact 

can be expressed through the equation I=Ci*Q/d. In this equation, the strength of an 

emission source, Q, is linearly related to the impact, I, that it will have on a receptor 

located a distance, d, away. The effect of meteorological prevailing winds can be 

factored into this approach by establishing the constant, Ci

, as a function of the sectors 

relative to the receptor site. This relationship can be established by comparing Q/d 

values to modeled impacts, which are also dependent on prevailing wind patterns at the 

site of impact. By establishing a different constant for each sector, based on prior 

modeling results – in this case, CALPUFF results – we are in effect “scaling” Q/d results 

 

23 We calculated distances using the Haversine formula, which uses spherical geometry to calculate the 

distance between two points on the surface of a sphere. Because the Earth is not an exact sphere, use of 

this formula introduces a small amount of error — on the order of 0.5% — in the distance calculations for 

any two locations on the Earth’s surface (see http://mathforum.org/library/drmath for further details). 

24 On-road mobile sources contribute about 2% of the SO2 inventory nationally (See Figure 4-2 for regional 

breakdown) and were not considered significant enough to include in this analysis, which does not provide 

results to that level of precision. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-14

by CALPUFF-calculated source impacts. The absolute impacts produced are then 

dependent on the CALPUFF results, however the relative contributions of each source 

within a wind sector is established completely independent of the CALPUFF calculation, 

yielding a quasi-independent method of apportionment to add to our weight-of-evidence 

approach. 

To determine the appropriate constant for each wind sector relative to a given 

receptor, a linear regression analysis was performed on 778 sources in the eastern U.S. 

with emissions data available from the continuous emissions monitoring system (CEMS) 

for 2002. The Q/d values were calculated for these sources and compared with their 

modeled source impacts from the CALPUFF model (see Phase I modeling discussed in 

Appendix D). The sites were grouped by angle into “wind sectors” such that each wind 

sector had a best-fit line with as high a correlation coefficient (R2

) value as possible. 

Most sectors had an R2 above or near 0.90. The slopes of the resulting best-fit lines were 

used as the constants in the above equation.25

To calculate the impact that each state had on a given receptor, the area and nonroad SO2 emission sources were summed across the entire state, and the distance to the 

receptor site for those emission sources was calculated based on that state’s geographic 

center, adjusted for population density.26 In this way, the area and non-road emissions 

were treated as a single point source located at the population-weighted center of each 

state. These impacts were then added to the impact of the point sources that were 

calculated individually. The sum of area, non-road, and point source impacts for each 

state was used to compare the contributions relative to other states in the eastern U.S. and 

parts of Canada. 

The principal contributors to the MANE-VU receptors, according to this method, 

include the midwestern states of Indiana and Ohio, as well as Pennsylvania and New 

York. This is due not only to the large emissions from these states, but also to the 

predominantly westerly winds that carry Midwest pollution eastward (the Midwest was 

located in the wind sector with the highest Ci-value, five times that of the lowest Civalue). Table 4-1 shows the relative contribution of eastern states and Canadian 

provinces on several receptor sites in the region. Figure 4-12 and Figure 4-13 show the 

corresponding Q/d rankings across a set of northern and southern Class I areas in or near 

MANE-VU. 

 

25 The analysis resulted in best-fit lines that did not always go through the origin. By forcing the regression 

lines through the origin, we ensure that a source with zero emissions would correspond to zero impact at 

the receptor. After having forced the best-fit lines through the origin, R2 values remained greater than 0.77 

and changed less than 0.01 from the original regression. The changes to the slope were considered 

insignificant, with an average change of 4%, ranging from -11% to 16%; the extremes occurred for plots 

with relatively few points and on the low end of R-squared correlations. Some angle ranges were not 

associated with a wind sector because of insufficient data for that angle range. For example, there was a 

lack of data for Lye Brook Wilderness receptor in the 0-144o

 angle range. This angle sector and similar 

sectors lacking adequate data were assigned the lowest Ci-value amongst the other wind sectors of the same 

receptor site. The impact of this decision should be small given the relatively few sources in these 

directions and their tendency to be downwind of the receptor. 

26 Calculations using county-level emissions and distance to county centroid to receptor were compared to 

the approach used here. This added complexity, however, did not substantially change the predicted 

impacts nor the relative rankings among states. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-15

Table 4-1. 2002 SO2 CALPUFF-scaled Emissions over Distance Impact (μg/m3) 

STATE ACADIA LYE BROOK BRIGANTINE SHENANDOAH EMISSIONS 

Pennsylvania 0.19 0.30 0.38 0.43 1,090,562

Ohio 0.19 0.23 0.27 0.46 1,273,755

West Virginia 0.08 0.09 0.16 0.32 573,136

Maryland 0.05 0.06 0.24 0.21 292,970

New York 0.12 0.15 0.15 0.13 341,493

Indiana 0.11 0.11 0.14 0.18 914,039

North Carolina 0.07 0.06 0.14 0.26 510,452

Virginia 0.06 0.04 0.14 0.17 309,709

Georgia 0.07 0.07 0.11 0.14 605,040

Kentucky 0.06 0.06 0.11 0.14 521,583

Michigan 0.08 0.08 0.06 0.10 432,166

Illinois 0.07 0.07 0.07 0.10 642,264

Tennessee 0.04 0.04 0.07 0.09 423,705

New Jersey 0.02 0.02 0.14 0.07 64,437

Alabama 0.05 0.05 0.07 0.08 548,054

Texas 0.04 0.04 0.05 0.06 849,831

Florida 0.04 0.03 0.06 0.07 537,327

Massachusetts 0.08 0.02 0.03 0.05 123,754

South Carolina 0.04 0.02 0.05 0.07 262,867

Delaware 0.02 0.02 0.10 0.04 83,549

Missouri 0.04 0.04 0.05 0.05 361,911

Wisconsin 0.03 0.03 0.03 0.04 263,040

Maine 0.05 <0.01 <0.01 0.01 39,423

Kansas 0.01 0.01 0.01 0.01 136,104

New Hampshire 0.03 <0.01 0.01 0.01 53,772

Minnesota 0.01 0.01 0.01 0.01 124,151

Mississippi 0.01 0.01 0.01 0.02 126,456

Iowa 0.01 0.01 0.01 0.01 230,676

Connecticut 0.01 0.01 0.01 0.01 41,093

Oklahoma 0.01 0.01 0.01 0.01 139,327

Louisiana 0.01 0.01 0.01 0.02 346,170

Arkansas <0.01 <0.01 0.01 0.01 140,096

Nebraska 0.01 <0.01 <0.01 0.01 46,074

Rhode Island <0.01 <0.01 <0.01 <0.01 2,531

Vermont <0.01 <0.01 <0.01 <0.01 1,575

Dist. of Columbia <0.01 <0.01 <0.01 <0.01 1,715

Ontario 0.01 0.24 0.12 0.15 5,010

New Brunswick 0.15 0.01 0.02 0.02 1,261

Quebec 0.09 0.02 0.03 0.05 6,567

Nova Scotia 0.08 0.01 0.02 0.02 7,566

Newfoundland 0.01 <0.01 <0.01 0.01 15,287

Prince Edward Is. <0.01 <0.01 <0.01 <0.01 10,157
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-16

Figure 4-12. Ranked state percent sulfate contributions to Northeast Class I 

receptors based on emissions divided by distance (Q/d) results 

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

CanadaPAOHINNYMIWVILGAMAKYNCVAMDALTNTXMOFLWINJMESCNHDEKSMNIACTOKMSLANEVTARRIDC

Annual Average Contribution (μg/m3 SO4)

Acadia

Great Gulf

Lye Brook

Moosehorn

Figure 4-13. Ranked state percent sulfate contributions to Mid-Atlantic Class I 

receptors based on emissions divided by distance (Q/d) results 

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

OHPAWVNCCanadaMDINVAKYGANYILTNMIALNJTXFLDESCMOWIMAMSKSMNOKIALACTNHARNEMEDCRIVT

Annual Average Contribution (

μg/m3 SO4)

Brigantine

Dolly Sods

Shenandoah
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-17

It is difficult to draw firm conclusions from what is essentially an empirical 

relationship between emission source strength, distance and observed impacts at receptor 

sites, but the addition of the CALPUFF-derived scale factors to this approach yields 

important insights as to the abilities of fairly simple screening techniques to accurately 

predict potential contributions to downwind receptors. This is borne out by the high 

degree of correspondence between the relative contributions of regions as identified by 

this and other techniques shown in Chapter 8. 

4.2.2. Emissions times Upwind Probability 

The Emissions times Upwind Probability method of assessing contribution to 

pollution involves multiplying the back-trajectory calculated residence time probability 

for a grid cell with the total emissions – over the same time period – from that grid cell. 

The product is an emissions-weighted probability field that can be integrated within state 

boundaries to calculate relative probabilities of each state contributing to pollution 

transport. 

A back trajectory is the path that a parcel of air is calculated to have taken prior to 

its arrival at a given receptor (See Chapter 5). The back trajectories used in this study 

were calculated by the HYSPLIT system (Draxler, 1997 and 1998). Five years of back 

trajectories, calculated eight times per day results in 14,600 back trajectories. The back 

trajectories are 72-hours in length and have calculated endpoints, or locations, at hourly 

intervals that specify the air mass path. The endpoints from all trajectories are mapped 

into a matrix of residence times spent in individual grid cells over the five year period. 

The resulting sum expresses the likelihood that air spent time in a particular quarter 

degree longitude by quarter degree latitude grid cell over a domain between 25o

 and 57o

latitude and -110o to -50o longitude. These residence times are then multiplied by the 

MANE-VU base year SO2 emission inventory that has been allocated to a 12 km 

horizontal grid based on a Lambert Conformal projection.27 The resulting product matrix 

contains the SO2-weighted residence times that are then numerically integrated within the 

boundaries of each state to define a “contribution” for each state. This provides a relative 

ranking of contribution by state that can be used to compare with other methods of 

attribution.28

The area of analysis included states from Maine to Mississippi. Several states lie 

on the periphery of our available SO2 emissions field and were used in the study despite

an incomplete inventory of SO2 emissions for the far edges of each state; these included 

 

27 Since the latitude-longitude projection of the residence time grid is different than the Lambert conformal 

projection of the emissions grid, there is not a one-to-one mapping. We therefore interpolated each 

residence time grid cell to increase the spatial resolution to 1/20o

 latitude by 1/20o longitude. Each 

residence time cell was then associated with the nearest SO2 emission cell to ensure that each SO2 emission 

component of the inventory was associated with the approximate residence time that was spent in nearest 

proximity to the emissions region. A distance of one-quarter degree between associated grid cells was used 

as a cutoff for the analysis. In other words, the product of a particular SO2 cell and residence time cell 

would not be used if the geographical distance between them was greater than one-quarter degree (latitude 

or longitude). 

28 Note that the absolute units are expressed as nmole/hr, which represent a fractional contribution of a grid 

cell’s emission rate that is likely to influence a downwind receptor. The physical meaning of this 

contribution is not clear, so this has been used in a relative sense only. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-18

Missouri, Arkansas, Mississippi, Alabama, and Georgia.29 Canada has significant SO2

emissions in the domain of the SO2 grid, hence contributions have been calculated for

portions of Ontario, Quebec and New Brunswick that were within the SO2 emission grid. 

Table 4-2 provides a ranking of state contributions and Figure 4-14 and Figure 4-15 show 

the ranked contribution for two groupings of Class I sites in or near MANE-VU. 

Table 4-2. 2002 SO2 Upwind Probability (percent contribution)

 ACADIA LYEBROOK BRIGANTINE SHENANDOAH 

West Virginia 0.06 0.07 0.09 0.19 

Ohio 0.09 0.11 0.10 0.12 

Pennsylvania 0.09 0.13 0.13 0.07 

Kentucky 0.04 0.05 0.06 0.09 

Indiana 0.05 0.05 0.05 0.06 

New York 0.07 0.11 0.04 0.02 

Virginia 0.03 0.02 0.06 0.06 

North Carolina 0.02 0.01 0.05 0.07 

Illinois 0.06 0.05 0.04 0.04 

Georgia 0.02 0.02 0.04 0.05 

Michigan 0.04 0.04 0.02 0.02 

Tennessee 0.02 0.01 0.02 0.04 

Maryland 0.02 0.02 0.04 0.03 

New Jersey 0.02 0.02 0.07 0.01 

Alabama 0.01 0.01 0.02 0.02 

South Carolina 0.01 0.01 0.02 0.02 

Wisconsin 0.02 0.02 0.01 0.01 

Missouri 0.01 0.01 0.01 0.01 

Delaware <0.01 0.01 0.02 <0.01 

Massachusetts 0.02 0.01 <0.01 <0.01 

New Hampshire 0.02 0.01 <0.01 <0.01 

Minnesota 0.01 0.01 <0.01 <0.01 

Connecticut 0.01 0.01 <0.01 <0.01 

Maine 0.02 <0.01 <0.01 <0.01 

Iowa 0.01 <0.01 <0.01 <0.01 

Dist. of Columbia <0.01 <0.01 <0.01 <0.01 

Arkansas <0.01 <0.01 <0.01 <0.01 

Mississippi <0.01 <0.01 <0.01 <0.01 

Vermont <0.01 <0.01 <0.01 <0.01 

Louisiana <0.01 <0.01 <0.01 <0.01 

Rhode Island <0.01 <0.01 <0.01 <0.01 

Texas <0.01 <0.01 <0.01 <0.01 

Canada 0.23 0.20 0.08 0.05 

 

29 These states still had significant areas that were not covered by the SO2 grid. Thus only a fraction of 

these states’ emissions were included in the total state contribution. The following are estimates of the area 

not covered by the SO2 grid: MO-20%, AR-10%, MS-25%, AL-20%, GA-5%. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-19

Figure 4-14. Ranked state percent sulfate contributions to Northeast Class I 

receptors based on emissions times upwind probability (E x UP) results 

0

1000

2000

3000

4000

5000

6000

CanadaOHPANYWVINILKYMIVAMDGAWINJNCTNNHMAMEMOALCTSCMNDEIAVTARDCRILAMSTX

Annual Average Contribution (nmoles/hr)

Acadia

Lye Brook

Moosehorn

Great Gulf

Figure 4-15. Ranked state percent sulfate contributions to Mid-Atlantic Class I 

receptors based on emissions times upwind probability (E x UP) results 

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

WVOHPAKYNCCanadaVAINGAILTNNJMDNYMIALSCMOWIDEMNDCIAARMACTMSNHLAMERIVTTX

Annual Average Contribution (nmoles/hr)

Brigantine

Shenandoah
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 4-20

References 

Ansari, A. S., and Pandis, S.N., “Response of inorganic PM to precursor concentrations,”

Environ. Sci. Technol., 32, 2706-2714, 1998. 

Davidson, C., Strader, R., Pandis, S., and Robinson, A., Preliminary Proposal to MARAMA 

and NESCAUM: Development of an Ammonia Emissions Inventory for the Mid-Atlantic 

States and New England. Carnegie Mellon University, Pittsburgh, PA. 7-Jan. 1999. 

Draxler, R.D. and Hess, G.D., “Description of the HYSPLIT-4 Modeling System,” 

NOAA Technical Memorandum ERL, ARL-224, Air Resources Laboratory, Silver 

Springs, Maryland, 24 pgs., 1997. 

Draxler, R.D. and Hess, G.D., “An Overview of the HYSPLIT-4 Modeling System for 

Trajectories, Dispersion, and Deposition,” Australian Meteorological Magazine, 1998, 

47, 295-308. 

Duyzer, J., “Dry Deposition of Ammonia and Ammonium Aerosols over Heathland,” J. 

Geophys. Res., 99(D9):18,757 – 18,763, 1994. 

EarthTech, 2004, http://src.com/calpuff/calpuff1.htm 

MARAMA 2004, http://www.marama.org/visibility/2002%20NEI/index.html 

NESCAUM, “Regional Haze and Visibility in the Northeast and Mid-Atlantic States,” 

January 2001a. 

NESCAUM, “Development of an Improved Ammonia Emissions Inventory for the 

United States,” December 2001b. 

NESCAUM, “2002: A Year in Review,” December, 2004. 

Odum, J.R., Jungkamp, T.P.W., Griffin, R.J., Flagan, R.C., and Seinfeld, J.H., “The 

Atmospheric Aerosol-forming Potential of Whole Gasoline Vapor,” Science, 276, 96-99, 1997. 

Strader, R., Anderson, N., and Davidson, C., Development of an Ammonia Inventory for 

the Mid-Atlantic States and New England, Progress Report, October 18, 2000, available 

online: http://marama.org/rt_center/MARAMAprogress10-18-00.pdf, 2000. 

USEPA, National Air Quality and Emission Trends Report, 1998, EPA 454/R-00-003, 

available online: http://www.epa.gov/oar/aqtrnd98/, 2000a. 

USEPA, National Air Pollutant Trends, 1900 – 1998, EPA 454/R-00-002, available 

online: http://www.epa.gov/ttn/chief/trends/trends98/trends98.pdf, 2000b. 

USEPA 2005, http://www.epa.gov/ttn/chief/eiinformation.html. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 5-1

5. DATA ANALYSIS TECHNIQUES 

Trajectory analyses have historically been used to trace the path of polluted air 

masses prior to their arrival at a given receptor site. Such analyses, by linking downwind 

measurements of ambient air quality with specific geographic areas upwind, can be very 

helpful in exploring the relative contribution of transported emissions from potential 

source regions on high and low pollution days. As with all of the tools and modeling 

techniques discussed in this report, trajectory analysis is not without some uncertainties 

and limitations. One such limitation is the fact that these analyses are typically unable to 

distinguish emission contributions from one point along the length of the trajectory from 

a different point along the path. In addition, the accuracy of any individual back 

trajectory calculation for a single observation or episode may be compromised by 

inherent limitations in the underlying Lagrangian trajectory models, which tend to 

become less accurate as the calculation progresses further back in time. Fortunately, a 

variety of techniques are available to mitigate these uncertainties and enhance confidence 

in the results obtained using trajectory analysis. These include techniques for 

triangulating results across multiple sites, ensemble techniques that combine the results of 

large numbers of back trajectories, clustering algorithms that group similar trajectories 

based on their spatial characteristics, and techniques for combining trajectory analyses 

with source apportionment models. All of these strategies can be useful in improving and 

refining traditional trajectory analyses. 

This chapter describes the results of back trajectory analyses that have been 

conducted to date for key pollutant species observed at MANE-VU and nearby receptor 

sites. In addition, we explore novel techniques for improving the accuracy of individual 

trajectories by grouping meteorologically similar back-trajectories into trajectory 

“clusters” and examining the relationship between the transport pathways defined by 

these clusters and downwind air quality observations. We then turn to source 

apportionment models which can be used to group available monitoring data for various 

components of PM2.5 in logical combinations that best explain the variation in observed 

species concentrations in terms of specific “source profiles.” These source profiles are 

used to distinguish the emissions from common pollution sources (e.g., mobile sources, 

coal combustion). The information obtained through source apportionment analysis can 

then be used in combination with back trajectory analysis to link specific geographic 

source regions with downwind air quality conditions and to establish the relative 

contribution of different source regions to visibility impacts at the receptor site. 

This chapter provides further description of several trajectory analysis techniques, 

before proceeding to a review of the insights gained to date by applying these techniques 

to analyze source regions for particulate pollution in the MANE-VU region. Preliminary 

results and interpretation are presented and used to support and bolster the basic 

conceptual model of regional haze outlined in Chapter 2. 

5.1. Trajectory Analysis 

The Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model 

(Draxler, 1997 and 1998) was used to calculate back trajectories for 13 sites in the 

northeastern United States. Most of these sites are located in Class I areas that are 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 5-2

subject to the Haze Rule, but several others are located in areas where potential 

nonattainment with the PM2.5 NAAQS warrant analysis. Back trajectories were 

calculated eight times per day for starting heights of 200, 500, and 1,000 meters above 

ground level using meteorological wind fields for the five-year period from 2000 through 

2004. Meteorological data from the National Oceanic and Atmospheric Administration 

(NOAA) Air Resources Laboratory (ARL) archives were used. These include wind 

fields from the Eta Data Assimilation System (EDAS), which cover North America with 

an 80 km spatial resolution and are based on 3-hourly variational analyses (Rolph, 2003). 

For the analyses presented here, we exclusively used the 500 meter EDAS trajectories 

from the baseline period (2000-2004). 

Each trajectory was matched with corresponding monitoring data collected as 

close in time as possible to the “start” time of the back trajectory calculation. The 

analysis included ambient measurements for PM2.5 and ozone (O3), as well as all 

particulate matter constituents that are routinely measured as part of the IMPROVE 

program. 

The resulting database of air quality monitoring results and associated back 

trajectories was used to develop several statistical measures of the probability or 

likelihood that a given upwind source region is associated with good or poor air quality at 

the receptor sites analyzed. Appendix A provides a detailed description of the metrics 

that were developed for this purpose and how they were calculated using both traditional 

trajectory analysis and cluster analysis techniques. This appendix also provides sitespecific results. 

5.1.1. Incremental Probability 

The incremental probability (IP) field represents a measure of the likelihood that a 

given source region contributes more than “average” to high concentrations of a 

particular pollutant at a downwind receptor site (see Appendix A for a more complete 

definition). This technique can also be used to identify locations that are less likely to 

contribute to poor air quality at a given receptor site, thus allowing for more robust 

conclusions to be drawn about likely source regions for individual fine particle 

constituents. 

Calculating IP fields for a subset of back trajectories within a complete sample 

can help further illuminate the different roles of different source regions. For example, it 

is interesting to note distinct differences between the IP field for back trajectories 

corresponding to the 10 percent highest observed sulfate values in the Northeast (three 

sites are shown that bracket the MANE-VU region’s Class I sites) and the IP field for 

trajectories corresponding to the lowest sulfate values in the Northeast (specifically, 

sulfate values in the lowest 10th percentile). Figure 5-1 and Figure 5-2 illustrate the IP 

fields for each set of observations, respectively. 

In Figure 5-1 and Figure 5-2, note that the red color indicates areas with greater 

probability of contributing to transport on the selected days. These show that the very 

highest observed sulfate values across the region are strongly associated with transport 

from a source region that encompasses the Ohio River Valley, western Pennsylvania, and 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 5-3

the urban East Coast corridor. On the days with the lowest measured sulfate, transport is 

associated with northwesterly winds from Canada and weather patterns off the Atlantic. 

Figure 5-1. Incremental Probability (Top 10% Sulfate) at 

Acadia, Brigantine and Lye Brook 2000-2004 

Acadia Brigantine Lye Brook

Figure 5-2. Incremental Probability (Bottom 10% Sulfate) at 

Acadia, Brigantine and Lye Brook 2000-2004 

Acadia Brigantine Lye Brook

5.1.2. Clustered Back-Trajectories 

Each of the IP fields shown in Figure 5-1 or Figure 5-2 incorporate results from 

over 14,000 back trajectories over the five-year period analyzed. In cases like these, 

where IP fields are calculated from a very large set of data points, the error in the 

calculation of any individual trajectory — which can be as high as 30 percent or more of 

the total transport distance involved in a given trajectory — is not likely to affect the 

overall result. Assuming that such errors are randomly distributed (i.e., no systematic 

bias exists in the calculations used by the trajectory model to calculate wind speed or 

direction), the use of large numbers of individual trajectories will effectively ensure that 

the random errors cancel out. To further minimize the effect of any errors with respect to 

individual trajectories, it is also possible to cluster large numbers of back trajectories 

according to their three-dimensional similarity (see Appendix A for a detailed description 

of several methodologies used). Figure 5-3 shows residence-time probability fields for 

clusters of similar back trajectories grouped according to their proximity to unique 

meteorological pathways. This metric yields probabilistic representations of the 

meteorological pathways which were most likely to be associated with the highest 

observed sulfate concentrations at the receptor site. Such probabilistic representations 

reduce the reliance on any one back trajectory and ensure that the general pattern used to 

associate a transport pathway with a downwind receptor site is more likely to be accurate. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 5-4

Figure 5-3. Proximity based cluster with the highest associated sulfate value for 

three sites in the MANE-VU region, Acadia (sulf=3.19 μg/m3), Brigantine 

(sulf=6.79 μg/m3), and Lye Brook (sulf=3.92 μg/m3) 

Acadia Brigantine Lye Brook

5.1.3. Cluster-Weighted Probability 

The clusters derived above can be used individually or combined in an “ensemble 

cluster” approach similar to how individual trajectories are combined to develop the IP 

metric. This second method for associating transport patterns with downwind pollution 

measurements involves using all clusters generated by the clustering algorithms described 

in the preceding section (and in detail in Appendix A) and weighting them by their 

average observed sulfate value. Simply averaging the residence-time probability of all 

clusters would yield the “everyday” probabilities that are used in calculating IP fields. 

Instead, weighting each cluster before the averaging process serves to highlight transport 

patterns that are associated with high sulfate levels at the receptor site, while 

downplaying patterns that are associated with low values. Figure 5-4 shows the resulting 

cluster-weighted probability (CWP) field. Results are similar to those obtained using the 

incremental probability metric described previously, but they now include all clusters, not 

just the high-day values. 

A noteworthy feature of the clustering process is that while it reduces uncertainty 

about prevailing transport patterns, it is not helpful in taking advantage of weather 

variations to identify specific source regions. Thus, results for a particular site should be 

interpreted as showing that observed air quality conditions have an increased probability 

of being associated with the transport of a specific pollutant, as opposed to being 

associated with a particular source region for a given pollutant. Put another way, it is 

difficult to make an association with a specific point along the pathway defined by a 

cluster. As with the IP approach described earlier, however, multi-site averaging can 

address this ambiguity by making it possible to triangulate on regions that are associated 

with the transport of pollution to multiple sites in different locations, as shown in Figure 

5-4. 

Both trajectory-based approaches (i.e., IP and CWP) have also been applied to 

Class I receptor sites in the nearby VISTAS region, which includes the Dolly Sods and 

Otter Creek Wilderness Areas in West Virginia as well as Shenandoah National Park and 

the James River Face Wilderness Area in Virginia. Results for the VISTAS Class I sites 

are presented at the conclusion of Appendix A. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 5-5

Figure 5-4. Cluster Weighted Probability at Acadia, Brigantine and Lye Brook 2000-2004 

Acadia Brigantine Lye Brook

5.2. Source Apportionment Models and Ensemble Trajectory Analysis 

of Source Apportionment Results 

Previous sections of this chapter have discussed a category of receptor-based 

assessment techniques known more generally as ensemble trajectory analysis. The latter 

category includes residence time analysis (RTA) as well as potential source contribution 

function (PSCF) and cluster analysis (see also Appendix A). In this section we turn to 

multivariate mathematical models for analyzing source contributions, such as chemical 

mass balance (CMB) models, principal component analysis (PCA), positive matrix 

factorization (PMF), and UNMIX. 

Receptor-based models begin with ambient air quality measurements at one or 

more receptor locations and work “backward” to identify logical combinations of 

pollutant species that best fit a “source profile.” Sources matching that profile are 

assumed to have contributed to the ambient pollutant concentrations historically observed 

at the receptor locations. These models are typically driven by variations in PM 

constituent concentrations across multiple observations at one or more sites. An 

advantage of PCA, PMF, and UNMIX is that source profiles do not need to be known in 

advance; however, this does mean that the results must be subjectively interpreted to 

identify and distinguish likely sources. 

Because of these complexities and because the multivariate models typically rely 

entirely on measurements of PM constituents without regard to meteorology, it can be 

extremely useful to consider results obtained through the ensemble trajectory techniques 

(which rely on meteorology only) when interpreting or evaluating the outputs from a 

multivariate modeling exercise. 

Appendix B provides details of numerous source apportionment and associated 

ensemble back trajectory analyses. These details cover results obtained for many of the 

most significant components of fine particulate mass and resulting light extinction. Here 

we focus on the “secondary sulfate” or “coal” source profile that was identified at nearly 

every site in the eastern United States. Secondary sulfate typically accounts for 30–60 

percent of overall fine particle mass and 60–80 percent of visibility impairment on the 

haziest days in the Northeast. 

Figure 5-5 shows results from one of the broadest studies conducted to date of 

sulfate sources and characteristics at nine eastern IMPROVE sites. The bars on the left 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 5-6

show the fraction of total sulfate measured at each site that is contributed by the 

“sulfate/coal” source profile as determined by the source apportionment models. The bars 

on the right show the fraction of each “sulfate/coal” source profile that is composed of 

sulfate. Figure 5-5 suggests that: (1) large sources contribute 70–90 percent of the total 

sulfate measured at these sites, and (2) that the contribution from these large sources 

consists of 50–90 percent sulfate. 

Figure 5-5. Sulfate characteristics of “secondary sulfate” (coal) 

sources identified at eastern sites 

0

10

20

30

40

50

60

70

80

90

100

ACAD BRIG DOSO GRSM JARI LYBR MACA SHEN WASH

Percent Sulfate at Site or in Source

% SO4 at site % SO4 in source

When large sulfate sources are associated with upwind states or regions through 

the use of back trajectories (Figure 5-6), it becomes clear that many Class I and urban 

sites in MANE-VU and adjoining areas are influenced by a common source region. 

These findings suggest that reductions in coal-related SO2 emissions would have 

substantial benefits in terms of improved visibility and reduced PM concentrations over a 

large part of the eastern United States and eastern Canada. 

This conclusion is further reinforced by comparing regions with significant 

emissions that match the “source profiles” generated by available mathematical modeling 

tools to regions identified through trajectory analysis as having a high probability of 

being upwind on days with high sulfate levels and high reconstructed extinction values. 

As shown in Figure 5-6, the degree of correspondence between these regions is 

substantial. This indicates that the “secondary sulfate/coal combustion” source profile 

prominent at several eastern sites is strongly linked to regions associated with the highest 

10 percent of recorded sulfate and reconstructed extinction values. It is noteworthy that 

the upwind regions identified in Figure 5-7 are derived from measurements spanning the 

entire IMPROVE network, suggesting that the source region for “secondary sulfate/coal 

combustion,” which is a dominant contributor to visibility impairment in parts of the 

eastern United States, is also a major contributor to observed sulfate and extinction 

outside the MANE-VU region. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 5-7

Figure 5-6. Incremental Probabilities for "Secondary Sulfate" (Coal) 

Sources in Eastern U.S. 

5.3. Trajectory Model Evaluation 

and Future Work 

The geographical correspondence 

exhibited in Figure 5-7 extends to the multisite average IP fields calculated for the 

MANE-VU region and shown previously in 

Figure 5-1. It also extends to the multi-site 

average IP field calculated using the ATAD 

model and shown in Figure B-30 in Appendix 

B. Essentially, both figures are versions of 

the same thing, but they do exhibit some 

subtle differences. These differences are 

highlighted in Figure 5-8 which compares the 

results of ATAD and HYSPLIT IP 

calculations for the top 10 percent of sulfate, 

selenium, and nickel observations at Lye 

Brook, Vermont. Sulfate is a secondary 

pollutant that tends to peak in the summer, 

whereas nickel and selenium are primary 

pollutants that typically peak in the 

wintertime. Ni and Se serve as excellent 

markers for residual oil and coal combustion 

respectively. The figure indicates strong 

agreement between the two models in terms of 

Figure 5-7. Comparison of probability fields for 

observed sulfate, “sulfate” source profiles for 

seven eastern sites and reconstructed deciviews
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 5-8

the IP fields they calculate for nickel, suggesting that — during wintertime — primary 

pollutants are tracked well by both techniques. There is less agreement between the IP 

fields for sulfate, suggesting either a southerly bias to the HYSPLIT calculations for this 

secondary pollutant, or a westerly bias to the ATAD results. 

Seasonal differences in the meteorology that affects Lye Brook and other East 

Coast sites during the summer versus during the winter may help to explain these model 

discrepancies. Some of the largest absolute differences between the ATAD and 

HYSPLIT estimates occur for the highest sulfate days. While there are many differences 

between the models, one key difference is in their trajectory start heights. The HYSPLIT 

trajectories all start at 500 meters above ground level while the ATAD model first 

estimates a “transport layer depth” (TLD) and then initiates the trajectory (while 

constraining subsequent trajectory endpoints) at a point roughly half way between ground 

level and the TLD. During summer, when the largest sulfate events occur, the resulting 

ATAD start heights are roughly twice as high as the 500 m HYSPLIT start heights (see 

Figure 5-9). Hence the ATAD calculations tend to extend over a greater distance to the 

west, while the summer HYSPLIT trajectories may be more reflective of flows that are 

nearer the surface and more frequently east of the Appalachian Mountains. Both flow 

regimes are important. In fact, Blumenthal et al. (1997) have observed that the highest 

ozone concentrations in the Northeast (which often coincide with episodes of high sulfate 

concentrations) tend to occur when surface flows up the Northeast urban corridor 

combine with synoptic flows over the Appalachian Mountains from the west, a pattern 

that is often accompanied by lower level nocturnal jets along the Northeast corridor and 

through gaps in the Appalachians. 

Figure 5-8. Comparison of IP contours generated by ATAD and HYSPLIT (both 

EDAS and FNL) for sulfate, nickel and selenium at Lye Brook 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 5-9

An extensive evaluation of the performance of HYSPLIT, ATAD, and Capita 

Monte Carlo trajectory models using a variety of different meteorological drivers, 

ensemble trajectory techniques, and performance tracers was recently conducted as part 

of the Big Bend Regional Aerosol and Visibility Observational (BRAVO) study 

(Pitchford et al., 2004). No one model consistently out-performed the others at that site, 

hence results from these and more sophisticated photochemical grid models (REMSAD 

and CMAQ) were merged to produce a best-estimate, “consensus” apportionment of 

sulfate in the BRAVO study. 

MANE-VU is using all available trajectory models, trajectory-related metrics, and 

improved understanding of transport phenomena to further explore and support the 

development of emission control strategies for reducing regional haze. 

Figure 5-9. ATAD Transport Layer Depth (TLD) by month. Color indicates the 

length of time prior to arriving at the receptor. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 5-10

References 

Blumenthal, D.L., Lurmann, F.W., Kumar, N., Ray, S.E, Korc, M.E., Londergan, R., 

Moore, G., Transport and mixing phenomena related to ozone exceedances in the 

Northeast U.S. Working Draft No. 1.1 prepared for Ozone Transport Assessment Group 

Air Quality Analysis Workgroup by Sonoma Technology, Inc., Santa Rosa, CA and Earth 

Tech, Concord, MA, STI-996133-1710-WD1.1, February 1997. 

Draxler, R.D. and Hess, G.D., “Description of the HYSPLIT-4 Modeling System,” 

NOAA Technical Memorandum ERL, ARL-224, Air Resources Laboratory, Silver 

Springs, Maryland, 24 pgs., 1997. 

Draxler, R.D. and Hess, G.D., “An Overview of the HYSPLIT-4 Modelling System for 

Trajectories, Dispersion, and Deposition,” Australian Meteorological Magazine, 47, 

295-308, 1998. 

Pitchford, M.L., Schichtel, B.A., Gebhart, K.A., Barna, M.G., Malm, W.C., Tombach, 

I.H., and Knipping, E.M., “Causes of Haze at Big Bend National Park – Results of the 

BRAVO Study and More” Regional and Global Perspectives on Haze: Causes, 

Consequences and Controversies – Visibility Specialty Conference, Air & Waste 

Management Association, Asheville, NC October 25-29, 2004. 

Rolph, G.D., Real-time Environmental Applications and Display sYstem (READY) 

Website (http://www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources 

Laboratory, Silver Spring, MD., 2003. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 6-1

6. CHEMICAL TRANSPORT MODELS 

Eulerian or “grid” models have traditionally served as the workhorse of air quality 

planning programs. These tools strive to be comprehensive in accounting for emissions, 

meteorological dynamics, chemical production, transformation, and destruction as well as 

wet and dry deposition and microphysical processes. With this degree of sophistication 

comes attendant uncertainty. Many of the more complex processes (e.g., cloud processes 

and boundary layer dynamics) are handled through parameterizations that attempt to 

approximate the real atmosphere at an appropriate level of detail. Chemical transport 

models for ozone and fine particles have improved markedly over the past several years 

as various groups have developed competing models and as the different strengths and 

weaknesses of these models help to shed light on various aspects of the underlying 

science. 

Two regional-scale air quality models have been evaluated and used by 

NESCAUM to perform air quality simulations. These are the Community Multi-scale Air 

Quality modeling system (CMAQ)30 and the Regional Modeling System for Aerosols and 

Deposition (REMSAD).31 Appendix C provides detailed descriptions of these models 

and of their use by NESCAUM, together with performance evaluations and preliminary 

results. A brief overview of the two modeling platforms in terms of their relevance to 

future SIP work is provided here, along with highlights of the findings. 

6.1. Chemical Transport Model (CTM) platforms – Overview

Both REMSAD and CMAQ are being used with a 12 km grid32 in the eastern U.S. 

domain (see Figure 6-1(b)). Air quality is modeled on 22 vertical layers with hourly 

temporal resolution for the entire calendar year 2002. REMSAD has simplified 

chemistry but allows for emissions tracking of sulfate, nitrate, and mercury through a 

tagging feature that calculates the contribution of specific sources to ambient 

concentrations, visibility impacts, and wet or dry deposition. REMSAD has shown good 

performance when reproducing annual or seasonal statistics for sulfate and mercury 

chemistry, while CMAQ has shown good performance for multiple species. A new 

release of CMAQ (version 4.5) may improve performance for sulfate, nitrate and 

organics over what Appendix C presents and will be used with the quality-assured 

meteorology and emission inventory inputs described below for final SIP submissions in 

2007 or 2008. 

Meteorological inputs have been developed by the University of Maryland 

(UMD) using the Fifth-Generation Pennsylvania State University/National Center for 

Atmospheric Research (NCAR) Mesoscale Model (MM5) system.33 A modified 

Blackadar boundary layer scheme is used as well as physics options including explicit 

representations of cloud physics with simple ice microphysics (no mixed-phase 

processes) and the Kain-Fritsch cumulus parameterization. 

 

30 See Byun and Ching, 1999. 

31 See ICF/SAI, 2002. 

32 12 km grid describes a 12 by 12 km grid cell 

33 http://www.mmm.ucar.edu/mm5/ 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 6-2

The New York Department of Environmental Conservation and NESCAUM are 

processing emissions inputs using the Sparse Matrix Operator Kernel Emissions 

(SMOKE) Modeling System. To model biogenic emissions, SMOKE uses the Biogenic 

Emission Inventory System, version 2.3 (BEIS2) and version 3.09 and 3.12 (BEIS3). 

SMOKE has also been integrated with the MOBILE6 model for on-road emissions. 

MANE-VU has developed a quality-assured 2002 emissions inventory which is being 

merged with the regional inventories for other RPOs in order to provide a comprehensive 

emissions inventory for the entire Northeast domain shown in Figure 6-1(b). 

A dynamic 3-dimensional boundary condition feeds ambient concentration fields 

in at the domain boundaries which are representative of actual concentrations during 

2002. This dynamic boundary condition was developed by applying the output of a 

global model run (Park et al., 2004) with 4 degree longitude by 5 degree latitude 

horizontal resolution at the boundaries of the 36 km grid domain shown in Figure 6-1(a). 

The results of this annual simulation are then applied at the boundary of our 12km grid 

domain, ensuring acceptable representation of the general trends and sulfate patterns that 

were present during the simulation period. 

Figure 6-1. Modeling domains used in NESCAUM air quality modeling studies. 

(a) Domain 1: 36 km National US grid domain with location of 12 km grid domain highlighted; 

(b) Domain 2: 12km Northeast US grid domain. The gridlines are shown at 180 km intervals 

(5 x 5 36 km cells or 15 x 15 12 km cells). 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 6-3

6.2. Preliminary Results 

CMAQ has been run for a complete set of baseline simulations including 2002, 

2009 and 2018. These preliminary runs are described in greater detail in Appendix C, but 

include inventory and meteorological drivers which will be updated for final SIP 

submissions. Nonetheless, these preliminary results suggest that implementation of 

existing regulations (including USEPA’s Clean Air Interstate Rule, or CAIR) will 

continue to yield significant improvements in visibility over the next decade, primarily as 

a result of regional sulfate reductions (See Figure 6-2 a and b below for visibility 

improvement and see Figure C-27 in Appendix C for sulfate mass reductions). Despite 

these potential improvements, not all MANE-VU Class I areas are anticipated to achieve 

uniform progress goals as described by current USEPA guidance.34 Brigantine 

Wilderness Area in New Jersey is projected to fall about a half deciview short of the 

uniform rate under existing emission reduction plans. 

A significant difference between the CMAQ and the REMSAD results presented 

here is that NESCAUM has taken the additional step of reprocessing the SO2 emission 

sources from each state such that these model inputs are formatted to take advantage of 

 

34 We note that uniform progress goals do not necessarily dictate visibility levels required by statute, but do 

represent a point of comparison for states when establishing reasonable progress goals toward our national 

visibility goal of no anthropogenic visibility impairment by 2064. 

Figure 6-2(a) and (b): CMAQ Integrated SIP Modeling Platform simulation results for 2002, 2009 

and 2018 relative to Uniform Progress Goals calculated according to current USEPA guidance 

for (a) Northeast Class I sites in MANE-VU and (b) Mid-Atlantic Class I sites in or near MANE-VU.

17

18

19

20

21

22

23

24

25

26

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Deciview

Acadia Uniform Goal

Camp Dodge Uniform Goal

Lye Brook Uniform Goal

Moosehorn Uniform Goal

Acadia Modeled

Camp Dodge Modeled

Lye Brook Modeled

Moosehorn Modeled
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 6-4

REMSAD’s tagging capabilities. Thus, all SO2 emissions included in the model for the 

eastern half of the country, Canada and the boundary conditions have been tagged 

according to state of origin. This allows for a rough estimation of the total contribution 

from elevated point sources in each state to simulated sulfate concentrations at eastern 

receptor sites. The tagging scheme employed for this analysis is illustrated in Figure 6-3. 

Using identical emission and meteorological inputs to those prepared for the Integrated 

SIP (CMAQ) platform, REMSAD was used to simulate the annual average impact of 

each state’s SO2 emission sources on the sulfate fraction of PM2.5 over the northeastern 

United States. 

Results of these tagged runs indicate that elevated point sources in Pennsylvania, 

Ohio, and New York contribute significantly, on an annual basis, to sulfate 

concentrations at all MANE-VU sites. Northern sites (e.g., Acadia) are more influenced 

by sources in upper midwestern states (e.g., Wisconsin and Michigan) whereas southern 

sites like Brigantine are more influenced by sources in more southerly states such as West 

Virginia, Maryland, and Virginia. Shenandoah, a VISTAS Class I site appears to be most 

strongly influenced by sources in Ohio, Pennsylvania, and West Virginia, followed by 

other nearby Southeast and Midwest states. Figure 6-4 through Figure 6-7 present these 

results showing the breakout of sulfate by individual tag. Note that the large “other” 

fraction of sulfate includes all sources outside the analysis domain, which includes some 

portions of the VISTAS and CENRAP RPO, Northern and Western Canada in addition to 

all other (i.e., inter-continental) sources of SO2. Figure 6-8 shows similar results 

summarized by RPO for the 20% worst days. 

Figure 6-2(b). 

22

23

24

25

26

27

28

29

2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

Deciview

Brigantine Uniform Goal

Dolly Sods Uniform Goal

Shenandoah Uniform Goal

Brigantine Modeled

Dolly Sods Modeled

Shenandoah Modeled
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 6-5

Figure 6-3. REMSAD modeling tagging schemes. 

(black: group 1, red: group 2, and blue: group 3) 

Note: Sulfur species from anthropogenic emission sources are tagged by states for three sets 

of tags. Tag group 3 also includes boundary conditions. The color of the numbers represents 

tag groups (black: group 1, red: group 2, and blue: group 3) 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 6-6

Figure 6-4. 2002 Eastern states’ contribution to annual PM sulfate in Acadia, ME 

OTHER

33.7%

MA

10.1% CANADA

8.7%

PA

6.8%

ME

6.5%

OH

5.6%

NY

4.7% WV

2.4%

NH

2.2%

MD

2.2%

IN

2.1%

MI

2.0%

VA

1.5%

NC

1.5%

NJ

1.4%

CENRAP

0.9%

IL

1.4% KY

1.2%

DE

1.0%

GA

0.7%

CT

0.8%

WI

0.8% RI

0.3%

DC

0.0%

MS

0.0%

VT

0.1%

TN

0.6%

AL

0.3%

SC

0.4%

Figure 6-5. 2002 Eastern states’ contribution to annual PM sulfate in Brigantine, NJ 

PA

12.8%

OH

9.1%

CANADA

NY 7.1%

5.6%

MD

5.0%

WV

4.7%

CT

0.5%

ME

0.2%

NH

0.6%

VT

0.1% MS

0.0%

DC

0.0%

RI

0.1%

WI

1.2%

CENRAP

1.1%

AL

1.1%

SC

1.7%

IL

KY 1.8%

2.3%

TN

1.6%

GA

2.3% MA

2.7%

MI

2.8% DE

3.2%

IN

3.3%

NJ

4.0%

NC

4.2%

VA

4.3%

OTHER

16.7%
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 6-7

Figure 6-6. 2002 Eastern states’ contribution to annual PM sulfate in Lye Brook, VT 

CANADA

12.4%

PA

11.7%

OH

9.9%

NY

9.0%

IN

3.9%

OTHER

19.0%

WV

3.7%

SC

0.6%

CT

0.6%

AL

0.8%

RI

0.1%

MS

0.0% DC

0.0%

ME

0.3%

TN

1.2%

VT

1.0%

DE

0.9%

NJ

1.4%

WI

CENRAP 1.5%

1.7%

GA

1.3%

NH

1.7% NC

VA 1.9%

2.0% KY

2.2%

IL

2.4% MA

2.4%

MD

2.7% MI

3.7%

Figure 6-7. 2002 Eastern states’ contribution to annual PM sulfate in Shenandoah, VA 

OTHER

12.4%

PA

12.0%

WV

9.6%

VA

6.2%

IN

5.2%

OH

14.8%

NC

4.8%

NH

0.1%

MS

0.1%

CT

0.1%

ME

0.0% RI

0.0% VT

0.0%

DC

0.0%

DE

0.6%

NJ

0.5%

MA

0.3% SC

1.3%

CENRAP

1.5%

AL

1.8%

WI

1.1%

NY

2.0%

IL

2.4%

TN

2.7%

MI

3.2%

GA

3.5% KY

4.4%

CANADA

4.7% MD

4.8%
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 6-8

Figure 6-8. Comparison of Sulfate Extinctions on 20% Worst Visibility Days 

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

DOSO SHEN BRIG LYBR GRGU ACAD MOOS

MANEVU MIDWEST VISTAS CENRAP CANADA OUTSIDE DOMAIN
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 6-9

References 

Byun, D.W., and Ching, J.K.S., Science Algorithms of the EPA Models-3 Community 

Multiscale Air Quality (CMAQ) Modeling System, EPA/600/R-99/030, March 1999. 

Park, R. J., Jacob, D. J., Field, B. D., Yantosca, R. M., and Chin, M., Natural and 

transboundary pollution influences on sulfate-nitrate-ammonium aerosols in the United 

States: implications for policy , J. Geophys. Res., D15204, 10.1029/2003JD004473, 

2004.

SAI, User’s Guide to the Regional Modeling System for Aerosols and Deposition 

(REMSAD), Version 7, ICF Consulting/SAI, San Francisco, CA, 2002. 
 Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 6-10
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-1

7. LAGRANGIAN DISPERSION MODELS 

Dispersion models are commonly used to study the impacts of pollutant plumes or 

specific point source emissions on surrounding areas. The scale of these models has 

traditionally been limited to a few hundred kilometers because of a perceived lack of 

ability to accurately reproduce horizontal dispersion beyond these distances. Recent 

advances in the CALPUFF system (USEPA, 2006) — including enhancements to its 

horizontal diffusion and dispersion algorithms as well as the addition of chemical 

transformation parameterizations — have resulted in improved performance over much 

greater distances. In fact, the most recent proposed guidance for implementing the 

BART (Best Available Retrofit Technology) requirements of the Regional Haze Rule 

provide for the use of CALPUFF to analyze dispersion over distances exceeding 200 km 

as long as a detailed modeling protocol is included for approval by the appropriate 

reviewing authority (40 CFR Part 51, pg. 25194, May 5, 2004). 

Appendix D provides specific information related to two CALPUFF platforms 

that have been developed for a large domain (see Figure 7-1) by the Vermont Department 

of Environmental Conservation (VT DEC) Air Pollution Control Branch and by the State 

of Maryland’s Department of the Environment (MDE) and Department of Natural 

Resources (MDNR) with contract assistance provided by Environmental Resources 

Management (ERM). Appendix D contains detailed descriptions of the two platforms; 

the processing and evaluation of both MM5- and National Weather Service (NWS)-based 

meteorological data; the processing and evaluation of CEMS (Continuous Emissions 

Monitoring System)- and 2002 RPO-based emissions data; performance evaluations of 

Figure 7-1. CALPUFF modeling domain utilized by MANE-VU 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-2

the overall modeling system; preliminary results of modeling to determine annual 

average and maximum 24-hour impact by individual unit and by state; and discussion of 

the future application of these platforms to the BART program. This chapter provides an 

overview of the two modeling platforms, a summary of initial results, and a brief analysis 

of the differences between the two platforms. 

While CALPUFF will certainly play a role in helping MANE-VU assess potential 

visibility impacts for BART-eligible sources, the development of twin CALPUFF 

platforms utilizing both MM5-based and NWS-based meteorological drivers further 

expands the suite of analytical tools available for assessing contributions — at both the 

facility and state level — to downwind visibility impairment in the MANE-VU region. 

7.1. Platform Overview 

The VT DEC developed meteorological inputs for CALPUFF using observationbased inputs (i.e., rawinsonde and surface measurements) from the NWS and by applying 

CALMET. VT DEC also developed hourly emissions and exhaust flow data from the 

Acid Rain Program’s CEMS data files for 869 large electric generating units (EGUs). 

These emissions data were utilized as inputs to CALPUFF, along with emissions data for 

four additional source sectors: non-EGU point sources, mobile (on-road), mobile (offroad), and general area sources. The emission inputs for these source sectors were 

derived from the 2002 RPO inventories. 

The MDNR and MDE developed meteorological inputs for CALPUFF using 

MM5 data developed by the University of Maryland for the MANE-VU and Ozone 

Transport Commission SIP modeling work. The Maryland agencies utilized the CEMS 

data files developed by VT DEC, and independently developed emissions and source 

parameters for the other four source sectors based on the same inter-RPO 2002 

inventories. 

Both platforms were used to model the entire calendar year 2002. These 

simulations have been configured to provide estimates for both individual source impacts 

and cumulative state impacts and to allow for inter-platform comparisons. The modeling 

domain has been designed to be consistent with the other modeling systems described in 

this report (e.g., REMSAD, CMAQ), so that conclusions regarding the most significant 

sources of sulfate-related visibility impacts in MANE-VU can be compared. Consistency 

across a broad range of approaches will add credibility to the conclusions reached in the 

overall contribution assessment. 

7.2. CALPUFF Modeling Results for Individual Sources 

To explore differences between the two CALPUFF modeling platforms, each was 

used to create a ranked list of the 100 emissions sources that contribute most to ambient 

sulfate levels at each of several eastern Class I sites. Of the 100 top sources identified for 

the Brigantine Wilderness Area, 70 sources appeared on the lists generated by both 

platforms. At Acadia, Lye Brook, and Shenandoah, there was even more agreement 

between the model results, with both platforms identifying 78, 76, and 85 out of 100 of 

the same top sources for each of these sites, respectively. Figure 7-2 shows the 

correlation between estimated annual average impacts for the sources that were identified 

by both platforms as among the top 100 sulfate contributors. While the 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-3

NWS/rawindsonde-based meteorology consistently produced slightly lower estimates of 

impact than the MM5-based platform, the correlations are relatively robust, ranging from 

0.89 at Brigantine to 0.93 at Lye Brook. 

Overall, the CALPUFF modeling results to date demonstrate reasonably good 

comparability between the two platforms (as illustrated by Figure 7-2 and Table 7-1), but 

they also suggest a consistent pattern of under prediction for one platform relative to the 

other. 

7.3. CALPUFF Modeling Results Overview 

Table 7-1 provides further comparisons of the results of CALPUFF modeling 

utilizing the two different platforms described earlier in this chapter: VT DEC 

(NWS/rawinsonde-based meteorology) and Maryland (MM5-based meteorology).35 The 

table summarizes annual average sulfate concentrations by source category for each of 

the two platforms relative to observed concentrations. 

Table 7-1. CALPUFF Overall Modeling Summary 

Annual Average SO4 Ion Concentration (μg/m3) 

NWS/Rawinsonde-based Meteorology MM5-based Meteorology 

CEMS 

EGU 

Non-CEMS 

Point Area/Mobile Total 

CEMS 

EGU 

Non-CEMS 

Point Area/Mobile Total 

Observed

Shenandoah 2.271 0.412 0.106 2.789 2.98 0.46 0.22 3.66 4.61 

Brigantine 1.847 0.421 0.257 2.526 2.6 0.51 0.38 3.48 4.06 

Acadia 0.965 0.385 0.218 1.569 1.42 0.42 0.28 2.13 1.86 

Lye Brook 1.178 0.342 0.178 1.698 1.65 0.36 0.25 2.26 2.17 

Generally, the NWS/rawinsonde platform predicts lower sulfate ion 

concentrations than the MM5 platform. On an annual average basis, the concentrations 

predicted using the MM5 platform are much closer to observed values than the 

concentrations predicted using the NWS/rawindsonde platform. 

7.4. CALPUFF Results for Ranked State Sulfate Contributions 

This section focuses on the ranked contribution of emissions from individual 

states to overall sulfate levels at specific receptor sites (additional results are summarized 

in a number of different ways in Appendix D). The rankings were calculated by summing 

impacts from EGUs included in the 2002 data base for each state. State contributions are 

then sorted by total annual impact. Predicted annual average sulfate ion concentrations 

from other source sectors were added to these data in Table 7-2(a-d) for both platforms. 

As in previous chapters, estimated contributions to receptor impact by state (using the 

results presented in Table 7-2) are depicted graphically in Figure 7-3 and Figure 7-4 for 

the observation-based and MM5-based platforms, respectively. States are ranked along 

the horizontal axis by averaging the individual results calculated for each state using the 

two CALPUFF platforms. 

 

35 The Maryland Department of the Environment is contributing toward this work through the Maryland 

Department of Natural Resources and their contractor ERM, Inc. who have developed the MM5-based 

meteorology and CALPUFF platform. 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU 

Figure 7-2. Correlation between MM5-based source contributions (Maryland/ERM) and NWS/rawinds

contributions (VT DEC) for common EGUs modeled at four receptor sites in or near MAN

Acadia

y = 1.6062x - 0.0003

R2 = 0.8988

0

0.005

0.01

0.015

0.02

0.025

0.03

0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018

VTDEC Modeled Annual SO4 Ion Impact (ug/m3)

ERM Modeled Annual SO4 Ion Impact (ug/m3)

Brigantine

y = 1.5134x - 0.0003

R2 = 0.8899

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0.045

0.05

0 0.005 0.01 0.015 

VTDEC Modeled Annual SO4 Ion Impa

ERM Modeled Annual SO4 Ion Impact (ug/m3)

Lye Brook

y = 1.3802x + 0.0002

R2 = 0.9333

0

0.005

0.01

0.015

0.02

0.025

0.03

0.035

0.04

0 0.005 0.01 0.015 0.02 0.025 0.03

VTDEC Modeled Annual SO4 Ion Impact (ug/m3)

ERM Modeled Annual SO4 Ion Impact (ug/m3)

Shenandoah

y = 1.6379x - 0.0023

R2 = 0.9143

0

0.010.020.030.040.050.060.070.08

0 0.005 0.01 0.015 0.02 0.025 0.03 

VTDEC Modeled Annual SO4 Ion Impa

ERM Modeled Annual SO4 Ion Impact (ug/m3)
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-5

Table 7-2a. Sulfate Ion Impacts by State (Annual Average) 

Acadia National Park 

NWS-based Meteorology (VT DEC) MM5-based Meteorology (MDE/MDNR) 

 μg/m3

μg/m3

STATE 

CEM 

PT 

NonCEM PT Area/ 

Mobile 

TOTAL 

PT 

CEM 

PT 

Non-CEM 

PT 

Area/ 

Mobile TOTAL 

AL(a) 0.0086 0.0013 0.0003 0.0102 0.0139 0.0009 0.0011 0.0159

AR(a) 0.0039 0 0 0.0039 0.0054 0.0020 0.0010 0.0083

CT 0.0041 0.0012 0.0085 0.0138 0.0074 0.0011 0.0072 0.0156

DC 0.0001 0.0001 0.0002 0.0004 6.9E-05 0.0001 0.0003 0.0005

DE 0.0087 0.002 0.0008 0.0115 0.0093 0.0109 0.0018 0.0219

GA(a) 0.0142 0.0008 0.0005 0.0155 0.0259 0.0009 0.0019 0.0287

IA 0.0097 0.0122 0.0001 0.0219 0.0149 0.0120 0.0030 0.0299

IL 0.0342 0.0157 0.0004 0.0504 0.0486 0.0172 0.0034 0.0693

IN 0.0758 0.0103 0.001 0.087 0.1089 0.0119 0.0099 0.1307

KS(a) 0.0081 0 0 0.0081 0.0137 0.0012 0.0010 0.0159

KY 0.0411 0.0054 0.0023 0.0487 0.0632 0.0038 0.0069 0.0740

MA 0.0653 0.0127 0.0579 0.136 0.0860 0.1544 0.0773 0.3176

MD 0.0398 0.0019 0.0034 0.0451 0.0780 0.0062 0.0040 0.0882

ME 0.0032 0.0243 0.0294 0.057 0.0030 0.0356 0.0236 0.0622

MI 0.0611 0.0083 0.0031 0.0726 0.0656 0.0095 0.0093 0.0844

MN 0.0089 0.0043 0.0005 0.0137 0.0107 0.0022 0.0023 0.0151

MO 0.014 0 0 0.014 0.0215 0.0115 0.0041 0.0371

MS(a) 0 0.0002 0.0002 0.0003 0 0.0002 0.0002 0.0004

NC 0.0342 0.0081 0.0014 0.0437 0.0554 0.0057 0.0019 0.0630

ND(a) 0 0.0009 0.0012 0.0021

NE(a) 0.0017 0 0 0.0017 0.0028 0 0.0009 0.0037

NH 0.0386 0.0022 0.0071 0.0479 0.0666 0.0020 0.0065 0.0750

NJ 0.013 0.0025 0.0076 0.0232 0.0187 0.0033 0.0133 0.0354

NY 0.0577 0.0118 0.0505 0.12 0.0736 0.0363 0.0578 0.1677

OH 0.1402 0.0081 0.0013 0.1496 0.2248 0.0457 0.0055 0.2759

OK(a) 0.0059 0 0 0.0059 0.0071 0.0015 0.0006 0.0092

PA 0.1383 0.0196 0.0126 0.1706 0.2354 0.0214 0.0156 0.2725

RI 0 0 0.0074 0.0074 5.9E-06 0.0007 0.0043 0.0050

SC 0.0092 0.003 0.001 0.0132 0.0134 0.0036 0.0012 0.0182

SD(a) 0.0009 0 0 0.0009 0.0012 2.8E-05 0.0009 0.0022

TN 0.0192 0.0045 0.0024 0.0261 0.0286 0.0076 0.0031 0.0393

TX(a) 0 0 0 0 1.1E-05 0 2.3E-05 3.5E-05

VA 0.0319 0.0082 0.0007 0.0407 0.0389 0.0081 0.0029 0.0499

VT 0 0.0004 0.0169 0.0173 4.0E-06 0.0004 0.0026 0.0030

WI 0.0152 0.0196 0.0005 0.0353 0.0254 0.0085 0.0019 0.0358

WV 0.0583 0.0053 0.0006 0.0642 0.0865 0.0086 0.0016 0.0966

Canada(b) 0 0.1914 0 0.1914 

Totals 0.96511 0.3854 0.21832 1.5688 1.45 0.44 0.28 2.17

Notes: 

(a) Only sources in that portion of the state within the RPO modeling domain were modeled. 

(b) 52 Canadian point sources > 250 tons/yr SO2 emissions during 2002 (from Canadian NPRI). 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-6

Table 7-2b. Sulfate Ion Impacts by State (Annual Average) 

Brigantine Wilderness Area 

NWS-based Meteorology (VT DEC) MM5-based Meteorology (MDE/MDNR) 

 μg/m3

μg/m3

STATE 

CEM 

PT 

NonCEM PT Area/ 

Mobile 

TOTAL 

PT 

CEM 

PT 

NonCEM PT Area/ 

Mobile TOTAL

AL(a) 0.0317 0.0055 0.0011 0.0383 0.0304 0.0017 0.0020 0.0341

AR(a) 0.0047 0 0 0.0047 0.0088 0.0032 0.0017 0.0137

CT 0.0041 0.0013 0.0099 0.0153 0.0044 0.0009 0.0063 0.0116

DC 0.0009 0.0004 0.0008 0.0021 0.0012 0.0005 0.0013 0.0030

DE 0.0395 0.0111 0.0073 0.0579 0.0524 0.0549 0.0138 0.1211

GA(a) 0.0576 0.0044 0.0030 0.0649 0.0672 0.0024 0.0057 0.0753

IA 0.0156 0.0176 0.0001 0.0333 0.0152 0.0137 0.0032 0.0321

IL 0.0521 0.0192 0.0005 0.0719 0.0535 0.0190 0.0043 0.0768

IN 0.1165 0.0125 0.0011 0.1302 0.1632 0.0162 0.0128 0.1921

KS(a) 0.0113 0 0 0.0113 0.0107 0.0009 0.0008 0.0124

KY 0.0846 0.0098 0.0039 0.0982 0.1285 0.0076 0.0135 0.1496

MA 0.0240 0.0049 0.0191 0.0480 0.0234 0.0406 0.0168 0.0808

MD 0.1351 0.0073 0.0165 0.1589 0.2191 0.0228 0.0210 0.2630

ME 0.0004 0.0017 0.0016 0.0037 0.0002 0.0017 0.0011 0.0030

MI 0.0579 0.0077 0.0028 0.0685 0.0810 0.0110 0.0120 0.1040

MN 0.0120 0.0056 0.0007 0.0183 0.0114 0.0025 0.0027 0.0166

MO 0.0179 0 0 0.0179 0.0202 0.0108 0.0036 0.0346

MS(a) 0 0.0006 0.0003 0.0009 0 0.0006 0.0005 0.0012

NC 0.1414 0.0360 0.0060 0.1835 0.1609 0.0160 0.0054 0.1823

ND(a) 0 0.0011 0.0015 0.0026

NE(a) 0.0031 0 0 0.0031 0.0025 0 0.0009 0.0035

NH 0.0064 0.0004 0.0012 0.0080 0.0100 0.0003 0.0010 0.0113

NJ 0.0426 0.0081 0.0518 0.1024 0.0625 0.0124 0.0805 0.1553

NY 0.0658 0.0120 0.0719 0.1497 0.0810 0.0307 0.0779 0.1896

OH 0.2611 0.0130 0.0017 0.2757 0.4297 0.0836 0.0088 0.5221

OK(a) 0.0068 0 0 0.0068 0.0077 0.0014 0.0007 0.0098

PA 0.2538 0.0460 0.0339 0.3336 0.4407 0.0553 0.0461 0.5421

RI 0 0 0.0042 0.0042 2.1E-06 0.0003 0.0016 0.0019

SC 0.0362 0.0139 0.0042 0.0542 0.0341 0.0101 0.0032 0.0475

SD(a) 0.0011 0 0 0.0011 0.0012 3.4E-05 0.0012 0.0024

TN 0.0477 0.0138 0.0049 0.0664 0.0630 0.0188 0.0061 0.0879

TX(a) 0 0 0 0 2.5E-07 0 2.9E-05 3.0E-05

VA 0.1442 0.0447 0.0035 0.1924 0.1577 0.0331 0.0119 0.2027

VT 0 0.0002 0.0033 0.0035 1.5E-06 0.0001 0.0006 0.0008

WI 0.0216 0.0312 0.0007 0.0535 0.0315 0.0106 0.0026 0.0447

WV 0.1499 0.0118 0.0016 0.1633 0.2340 0.0202 0.0046 0.2588

Canada(b) 0 0.0807 0 0.0807 

Totals 1.84732 0.42121 0.25746 2.526 2.61 0.51 0.38 3.49

Notes: 

(a) Only sources in that portion of the state within the RPO modeling domain were modeled. 

(b) 52 Canadian point sources > 250 tons/yr SO2 emissions during 2002 (from Canadian NPRI). 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-7

Table 7-2c. Sulfate Ion Impacts by State (Annual Average) 

Lye Brook Wilderness Area 

NWS-based Meteorology (VT DEC) MM5-based Meteorology (MDE/MDNR) 

 μg/m3

μg/m3

STATE 

CEM 

PT 

NonCEM PT Area/ 

Mobile 

TOTAL 

PT 

CEM 

PT 

NonCEM PT Area/ 

Mobile TOTAL

AL(a) 0.0151 0.0023 0.0005 0.0179 0.0209 0.0013 0.0015 0.0238

AR(a) 0.0053 0 0 0.0053 0.0072 0.0029 0.0015 0.0116

CT 0.0015 0.0004 0.0038 0.0057 0.0024 0.0006 0.0045 0.0075

DC 0.0001 0.0002 0.0003 0.0005 7.9E-05 0.0002 0.0004 0.0006

DE 0.0045 0.0017 0.0007 0.0068 0.0076 0.0123 0.0020 0.0219

GA(a) 0.0270 0.0016 0.0011 0.0296 0.0351 0.0012 0.0029 0.0392

IA 0.0151 0.0175 0.0001 0.0326 0.0184 0.0158 0.0041 0.0383

IL 0.0473 0.0173 0.0005 0.0651 0.0550 0.0208 0.0047 0.0805

IN 0.1039 0.0120 0.0011 0.1170 0.1369 0.0148 0.0128 0.1645

KS(a) 0.0115 0 0 0.0115 0.0167 0.0016 0.0013 0.0195

KY 0.0647 0.0075 0.0031 0.0753 0.0820 0.0047 0.0099 0.0967

MA 0.0106 0.0040 0.0125 0.0270 0.0161 0.0291 0.0203 0.0655

MD 0.0452 0.0025 0.0040 0.0518 0.0686 0.0088 0.0052 0.0826

ME 0.0001 0.0020 0.0017 0.0038 0.0003 0.0024 0.0018 0.0044

MI 0.0841 0.0113 0.0041 0.0995 0.0798 0.0121 0.0120 0.1039

MN 0.0130 0.0062 0.0007 0.0200 0.0147 0.0031 0.0035 0.0213

MO 0.0191 0 0 0.0191 0.0253 0.0140 0.0052 0.0445

MS(a) 0 0.0004 0.0002 0.0006 0 0.0006 0.0004 0.0011

NC 0.0424 0.0088 0.0016 0.0528 0.0680 0.0058 0.0022 0.0760

ND(a) 0 0.0014 0.0020 0.0035

NE(a) 0.0027 0 0 0.0027 0.0032 0 0.0012 0.0044

NH 0.0072 0.0007 0.0020 0.0098 0.0137 0.0008 0.0023 0.0167

NJ 0.0071 0.0017 0.0051 0.0139 0.0128 0.0029 0.0115 0.0272

NY 0.0637 0.0289 0.0586 0.1511 0.0985 0.0613 0.0842 0.2440

OH 0.2108 0.0112 0.0016 0.2237 0.2963 0.0649 0.0078 0.3690

OK(a) 0.0086 0 0 0.0086 0.0097 0.0020 0.0009 0.0127

PA 0.1918 0.0255 0.0169 0.2342 0.3050 0.0288 0.0219 0.3558

RI 0 0 0.0013 0.0013 1.4E-06 0.0002 0.0010 0.0012

SC 0.0088 0.0037 0.0013 0.0138 0.0133 0.0040 0.0014 0.0187

SD(a) 0.0014 0 0 0.0014 0.0017 4.3E-05 0.0014 0.0031

TN 0.0281 0.0065 0.0032 0.0378 0.0407 0.0098 0.0042 0.0546

TX(a) 0 0 0 0 8.4E-06 0 3.2E-05 4.0E-05

VA 0.0295 0.0088 0.0008 0.0391 0.0454 0.0104 0.0037 0.0596

VT 0 0.0006 0.0499 0.0505 4.0E-06 0.0017 0.0083 0.0100

WI 0.0229 0.0293 0.0007 0.0529 0.0351 0.0116 0.0028 0.0495

WV 0.0852 0.0079 0.0009 0.0939 0.1232 0.0121 0.0023 0.1375

Canada(b) 0 0.1211 0 0.1211 

Totals 1.1780 0.3416 0.1781 1.6977 1.65 0.36 0.25 2.27

Notes: 

(a) Only sources in that portion of the state within the RPO modeling domain were modeled. 

(b) 52 Canadian point sources > 250 tons/yr SO2 emissions during 2002 (from Canadian NPRI). 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-8

Table 7-2d. Sulfate Ion Impacts by State (Annual Average) 

Shenandoah National Park 

NWS-based Meteorology (VT DEC) MM5-based Meteorology (MDE/MDNR) 

 μg/m3

μg/m3

STATE 

CEM 

PT 

NonCEM PT Area/ 

Mobile 

TOTAL 

PT 

CEM 

PT 

NonCEM PT Area/ 

Mobile TOTAL

AL(a) 0.0521 0.0084 0.0018 0.0623 0.0504 0.0029 0.0034 0.0567

AR(a) 0.0074 0 0 0.0074 0.0087 0.0035 0.0019 0.0141

CT 0.0005 0.0002 0.0011 0.0018 0.0007 0.0001 0.0009 0.0017

DC 0.0004 0.0004 0.0008 0.0016 8.1E-05 0.0003 0.0009 0.0013

DE 0.0101 0.0029 0.0011 0.0141 0.0086 0.0136 0.0021 0.0243

GA(a) 0.0879 0.0056 0.0040 0.0975 0.0963 0.0032 0.0079 0.1073

IA 0.0192 0.0181 0.0001 0.0374 0.0152 0.0130 0.0036 0.0318

IL 0.0646 0.0222 0.0006 0.0874 0.0561 0.0189 0.0045 0.0794

IN 0.1782 0.0156 0.0015 0.1952 0.1907 0.0181 0.0155 0.2243

KS(a) 0.0137 0 0 0.0137 0.0091 0.0007 0.0006 0.0104

KY 0.1273 0.0135 0.0057 0.1465 0.1741 0.0106 0.0184 0.2031

MA 0.0036 0.0005 0.0020 0.0060 0.0029 0.0047 0.0023 0.0098

MD 0.1045 0.0116 0.0118 0.1280 0.1365 0.0373 0.0109 0.1847

ME 0 0.0004 0.0003 0.0007 2.8E-05 0.0003 0.0002 0.0006

MI 0.0830 0.0082 0.0036 0.0948 0.0860 0.0100 0.0125 0.1085

MN 0.0148 0.0055 0.0007 0.0210 0.0109 0.0023 0.0028 0.0160

MO 0.0255 0 0 0.0255 0.0180 0.0104 0.0034 0.0318

MS(a) 0 0.0009 0.0004 0.0013 0 0.0010 0.0007 0.0017

NC 0.1669 0.0251 0.0050 0.1970 0.2257 0.0148 0.0062 0.2467

ND(a) 0 0.0011 0.0016 0.0027

NE(a) 0.0038 0 0 0.0038 0.0023 0 0.0009 0.0032

NH 0.0010 0.0001 0.0002 0.0012 0.0013 5.3E-05 0.0002 0.0016

NJ 0.0102 0.0018 0.0046 0.0166 0.0119 0.0022 0.0071 0.0212

NY 0.0350 0.0027 0.0141 0.0519 0.0468 0.0141 0.0167 0.0776

OH 0.4678 0.0256 0.0027 0.4960 0.6483 0.1088 0.0114 0.7685

OK(a) 0.0080 0 0 0.0080 0.0081 0.0016 0.0009 0.0105

PA 0.2774 0.0354 0.0214 0.3342 0.4517 0.0318 0.0247 0.5082

RI 0 0 0.0004 0.0004 3.1E-07 2.9E-05 0.0002 0.0002

SC 0.0242 0.0117 0.0041 0.0401 0.0232 0.0093 0.0035 0.0359

SD(a) 0.0011 0 0 0.0011 0.0011 4.0E-05 0.0014 0.0025

TN 0.0781 0.0207 0.0073 0.1061 0.0929 0.0304 0.0086 0.1319

TX(a) 0 0 0 0 1.7E-07 0 3.2E-05 3.2E-05

VA 0.1102 0.0398 0.0047 0.1547 0.1124 0.0469 0.0263 0.1856

VT 0 0 0.0006 0.0007 3.6E-07 2.6E-05 0.0001 0.0002

WI 0.0259 0.0311 0.0007 0.0577 0.0289 0.0096 0.0026 0.0410

WV 0.2691 0.0259 0.0045 0.2995 0.4657 0.0402 0.0111 0.5170

Canada(b) 0 0.0781 0 0.0781 

Totals 2.271 0.412 0.106 2.789 2.98 0.46 0.22 3.66

Notes: 

(a) Only sources in that portion of the state within the RPO modeling domain were modeled. 

(b) 52 Canadian point sources > 250 tons/yr SO2 emissions during 2002 (from Canadian NPRI). 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-9

Figure 7-3a. Ranked state percent sulfate contributions to Northeast Class I 

receptors based on observation-based (VT) CALPUFF results 

0

0.05

0.1

0.15

0.2

0.25

0.3

PACaOH

NY

IN

MI

MA

W

KYILWINC

MD

VAMETNNH

IA

VT

GANJMNMO

SC

AL

KS

CT

DEOK

RI

NE

DCMS

AR

Annual Average Contribution (μg/m3) SO4

Acadia

Great Gulf

Lye Brook

Moosehorn

0

0.05

0.1

0.15

0.2

0.25

0.3

PACaOH

NY

0

0.05

0.1

0.15

0.2

0.25

0.3

PACaOH

NY

IN

MI

MA

W

KYILWINC

MD

VAMETNNH

IA

VT

GANJMNMO

SC

AL

KS

CT

DEOK

RI

NE

DCMS

AR

Annual Average Contribution (μg/m3) SO4

Acadia

Great Gulf

Lye Brook

Moosehorn

Figure 7-3b. Ranked state percent sulfate contributions to Mid-Atlantic Class I 

receptors based on observation-based (VT) CALPUFF results 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Annual Average Contribution (

OHPAWVINNCKYVAMDTNMIILNYCanadaGAWIALNJSCIADEMOMNMAKSOKCTNENHMERIVTDCMSAR

μg/m3 SO4)

Brigantine

Dolly Sods

Shenandoah
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-10

Figure 7-4a. Ranked state percent sulfate contributions to Northeast Class I 

receptors based on MM5-based (MD) CALPUFF results 

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

OHPANYMAINWVMIKYILMDNCVATNNHWIMOIAGAMENJDEALSCMNKSCTOKARVTNERINDSDMSDCTX

Annual Average Contribution (

μg/m3 SO4)

Acadia

Lye Brook

Figure 7-4b. Ranked state percent sulfate contributions to Mid-Atlantic Class I 

receptors based on MM5-based (MD) CALPUFF results 

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

OHPAWVMDNCINVAKYNYTNMIGANJILDEALMAWISCMIAMNARKSOKCTNHNENDSDDCMEMSRIVTTX

Annual Average Contribution (

μg/m3 SO4)

Brigantine

Shenandoah
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-11

7.5. Future work and potential uses of CALPUFF results for BART 

determinations 

Modeling efforts to date have provided a solid basis for contributing to a weightof-evidence assessment of state contributions. In addition, the two CALPUFF platforms 

can be used to evaluate the relative contributions to fine PM and visibility impacts of 

individual sources in the MANE-VU region. It is anticipated that MANE-VU will 

provide all states with a consistent set of modeling results from each of these platforms to 

serve as a preliminary basis for BART visibility determinations and states will have 

several options with regard to how these results are used: 

• States may accept the MANE-VU modeling as an adequate basis for determining 

whether BART controls at a facility are justified by its contribution to visibility 

degradation. 

• States may conduct additional modeling on their own to determine whether 

BART controls at a facility are justified by its contribution to visibility 

degradation. 

• States may require a source to conduct additional modeling to determine whether 

BART controls at a facility are justified by its contribution to visibility 

degradation. 

These options and the use of modeling results for BART determinations are 

discussed in more detail in the MANE-VU BART Resource Book (NESCAUM, 2006), and 

the reader is referred to that resource for additional information. 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 7-12

References 

NESCAUM, BART Resource Guide, Northeast States for Coordinated Air Use 

Management, 2006. 

USEPA, CALPUFF Modeling System, Available at: http://www.epa.gov/ttn/scram, 2006. 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 8-1

8. SYNTHESIS OF RESULTS USING DIFFERENT 

SOURCE ASSESSMENT TECHNIQUES 

By synthesizing results from a variety of data sources and analysis techniques 

MANE-VU has taken a first step toward identifying sources of visibility impairment in 

the Northeast generally, and toward understanding the role of transported sulfate in 

particular. The variety of approach and complexity of analytical tools utilized for this 

purpose provides numerous metrics and means of comparison into how SO2 emissions 

are chemically transformed, transported and combined with various local constituents of 

fine particle pollution in the MANE-VU region. Beyond reviewing these results, 

additional sections of this chapter describe opportunities for further synthesizing the 

available data to solidify a weight-of-evidence approach to implementing the contribution 

assessment and pollution apportionment requirements of the Haze Rule 

8.1. Ranked Contribution 

Chapter 4 of this report describes two crude methods of ranking state 

contributions based on the ratio of source emissions to source-receptor distance as well as 

the gridded product of emissions and upwind residence time probability. Chapter 5 

describes the qualitative evidence available from several different trajectory-based 

techniques and source apportionment studies. These include source region comparisons, 

source profile examinations, and the development of other techniques and metrics to 

support the more quantitative ranking techniques. Chapter 6 describes results obtained 

using Eulerian grid models such as the Regulatory Modeling System for Aerosols and 

Deposition (REMSAD) and the Community Multi-scale Air Quality (CMAQ) model. 

Ultimately these types of models are likely to yield the most definitive assessments of 

contribution from different sources. Chapter 7 explores the use of lagrangian puff 

dispersion models such as CALPUFF for estimating source contributions and compares 

two related but distinct versions of the CALPUFF modeling system that demonstrate the 

sensitivity of this tool to emissions and meteorology inputs. 

In Table 8-1 through Table 8-5 (and graphically in Figure 8-1), we have 

normalized the results obtained using five techniques for assessing state contribution by 

calculating the percentage contribution and plotted them on a common graph. The figure 

shows substantial consistency across a variety of independent analyses using techniques 

that are themselves based on the application of disparate chemical, meteorological and 

physical principles. Together, these findings create a strong weight-of-evidence case for 

identifying the most significant contributors to visibility impairment in MANE-VU Class 

I areas. 

In Figure 8-1, several features of the normalized results bear notice. First, we 

note that the apparent perfect agreement among the techniques for the “other” 

contribution that represents all emissions from outside the domain of study is a result of 

having substituted the REMSAD calculated “other” contribution for all of the other 

methods. REMSAD is the only method that has a means of developing a comprehensive 

estimate of the total out-of-domain contribution because the boundary condition used was 

derived from a global model run using global SO2 emissions estimates. It is also worth 

noting how high the “other,” or out-of-domain, contribution is to observed sulfate at 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 8-2

Acadia National Park. This is not surprising given how close Acadia is to the domain 

boundaries on both the northern and eastern edge. There may be some recirculation of 

in-domain SO2 emissions that leave the modeling domain and re-enter through the 

dynamic boundary condition, but lose their tag in the process. 

It is also worth noting the differences between the methods for certain states and 

Canada, such as Massachusetts and Maine in the case of Acadia, Maryland and Canada 

for Brigantine, Canada for Lye Brook, and Ohio and West Virginia for Shenandoah. 

Those states and Canada that are directly upwind a large fraction of the time, either 

because they are very large geographically or because they are very nearby, are likely to 

be treated differently by the percent-time-upwind method relative to the other methods. 

In addition, the CALPUFF models appear to underestimate the contribution from Canada 

relative to other methods. This is likely to result from an incomplete characterization of 

the total SO2 inventory for Canada relative to other methods that are based on the entire 

MANE-VU Canadian inventory. 

Table 8-1. Annual Average Sulfate Impact from REMSAD (%) 

RPO STATE ACADIA BRIGANTINE DOLLY SODS GREAT GULF LYE BROOK MOOSEHORN SHENANDOAH

CANADA 8.69 7.11 3.90 14.84 12.43 7.85 4.75 

CENRAP 0.88 1.12 1.58 1.65 1.67 0.82 1.48 

MANE-VU 36.17 34.83 14.81 27.83 31.78 30.08 20.59 

Connecticut 0.76 0.53 0.04 0.48 0.55 0.56 0.08 

Delaware 0.96 3.20 0.30 0.63 0.93 0.71 0.61 

District of 

Columbia 0.01 0.04 0.01 0.01 0.02 0.01 0.04 

Maine 6.54 0.16 0.01 2.33 0.31 8.01 0.02 

Maryland 2.20 4.98 2.39 1.92 2.66 1.60 4.84 

Massachusetts 10.11 2.73 0.18 3.11 2.45 6.78 0.35 

New Hampshire 2.25 0.60 0.04 3.95 1.68 1.74 0.08 

New Jersey 1.40 4.04 0.27 0.89 1.44 1.03 0.48 

New York 4.74 5.57 1.32 5.68 9.00 3.83 2.03 

Pennsylvania 6.81 12.84 10.23 8.30 11.72 5.53 12.05

Rhode Island 0.28 0.10 0.01 0.11 0.06 0.19 0.01 

MANE-VU 

Vermont 0.13 0.06 0.00 0.41 0.95 0.09 0.01 

MIDWEST 11.98 18.16 30.26 20.10 21.48 10.40 26.84 

Illinois 1.37 1.82 2.56 2.52 2.42 1.30 2.47 

Indiana 2.13 3.29 5.40 3.94 3.93 2.02 5.23 

Michigan 2.02 2.77 3.24 3.88 3.67 1.74 3.20 

Ohio 5.62 9.11 17.98 8.33 9.96 4.62 14.87 

MIDWEST 

Wisconsin 0.85 1.16 1.08 1.42 1.49 0.72 1.07 

VISTAS 8.49 21.99 36.75 12.04 13.65 6.69 33.86 

Alabama 0.32 1.07 2.13 0.65 0.81 0.25 1.77 

Georgia 0.67 2.32 3.71 1.27 1.31 0.56 3.47 

Kentucky 1.17 2.22 4.89 1.99 2.22 0.98 4.34 

Mississippi 0.01 0.04 0.08 0.03 0.04 0.01 0.07 

North Carolina 1.45 4.19 4.29 1.88 1.89 1.14 4.78 

South Carolina 0.43 1.69 1.04 0.64 0.56 0.36 1.30 

Tennessee 0.61 1.56 3.41 1.11 1.23 0.50 2.73 

Virginia 1.48 4.30 2.82 1.52 1.95 1.13 6.20 

VISTAS 

West Virginia 2.35 4.59 14.38 2.96 3.64 1.75 9.19 

OTHER 33.79 16.78 12.70 23.54 18.99 44.17 12.48 

TOTAL (μg/m3) 2.026 3.444 3.867 1.780 2.137 1.767 3.919 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU 

Table 8-2. Annual Average Sulfate Impact from Q/D (%) 

RPO STATE ACADIA BRIGANTINE DOLLY SODS GREAT GULF LYE BROOK MOOSEHOR

CANADA 11.91 6.01 0.00 8.97 12.00 18.77 CENRAP 1.74 1.64 1.59 2.33 1.99 1.35 

Arkansas 0.13 0.15 0.13 0.08 0.17 0.09 Iowa 0.29 0.19 0.24 0.40 0.32 0.24 Louisiana 0.02 0.03 0.02 0.02 0.03 0.02 Minnesota 0.22 0.13 0.16 0.30 0.24 0.13 

CENRAP 

Missouri 1.08 1.15 1.03 1.53 1.23 0.87 

MANE-VU 20.13 32.53 20.10 21.48 25.69 12.84 

Connecticut 0.34 0.33 0.11 0.74 0.38 0.21 Delaware 0.59 3.01 0.46 0.51 0.67 0.36 District of Columbia 0.01 0.05 0.02 0.01 0.02 0.01 Maine 1.74 0.15 0.08 0.71 0.15 1.13 Maryland 1.83 7.26 3.86 0.43 2.67 1.27 Massachusetts 2.89 0.95 0.46 4.61 1.06 1.33 New Hampshire 1.07 0.30 0.14 0.42 0.08 0.60 New Jersey 0.76 4.22 0.43 3.11 0.75 0.48 New York 4.02 4.61 1.93 3.67 6.71 2.83 Pennsylvania 6.64 11.57 12.58 6.62 13.07 4.50 Rhode Island 0.12 0.05 0.02 0.08 0.04 0.06 

MANE-VU 

Vermont 0.10 0.03 0.02 0.57 0.10 0.07 

MIDWEST 16.99 17.48 26.30 25.38 22.84 12.49 

Illinois 2.53 2.16 2.60 3.64 2.98 2.11 Indiana 3.94 4.24 5.17 6.01 5.01 2.91 Michigan 2.69 1.95 2.46 4.08 3.50 2.16 Ohio 6.63 8.34 15.06 9.94 9.98 4.51 

MIDWEST Wisconsin 1.19 0.79 1.00 1.71 1.38 0.80 

VISTAS 15.44 25.55 39.32 18.30 18.48 10.39 

Alabama 1.24 1.69 1.66 1.45 1.60 0.91 Georgia 2.36 3.28 3.18 2.62 2.82 1.63 Kentucky 2.07 3.36 3.99 3.18 2.79 1.50 Mississippi 0.19 0.24 0.22 0.22 0.24 0.14 North Carolina 2.27 4.16 9.03 2.59 2.69 1.44 South Carolina 1.29 1.62 0.95 1.14 0.94 0.70 Tennessee 1.45 2.14 2.49 1.74 1.92 1.06 Virginia 1.93 4.36 2.49 1.97 1.78 1.12 

VISTAS 

West Virginia 2.64 4.71 15.33 3.39 3.71 1.88 

OTHER36 33.79 16.78 12.70 23.54 18.99 44.17 TOTAL (μg/m3) 1.920 2.740 3.455 1.305 1.858 1.977 

36 OTHER is % from REMSAD result; Florida is considered within OTHER 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU 

Table 8-3. Annual Average Sulfate Impact from CALPUFF (NWS Observations) (%) 

RPO STATE ACADIA BRIGANTINE DOLLY SODS GREAT GULF LYE BROOK MOOSEHOR

CANADA 8.07 2.65 2.30 7.22 5.77 9.45 CENRAP 2.76 2.98 3.34 5.06 4.50 2.30 

Iowa 0.93 1.09 1.13 1.65 1.55 0.80 Kansas 0.34 0.37 0.41 0.64 0.55 0.28 Louisiana 0.00 0.00 0.00 0.00 0.00 0.00 Minnesota 0.58 0.60 0.62 1.16 0.95 0.49 Missouri 0.59 0.59 0.81 1.00 0.91 0.49 Nebraska 0.07 0.10 0.11 0.14 0.13 0.06 

CENRAP 

Oklahoma 0.25 0.22 0.26 0.47 0.41 0.20 

MANE-VU 27.41 29.17 16.21 20.91 26.52 21.11 

Connecticut 0.58 0.50 0.03 0.26 0.27 0.41 Delaware 0.48 1.90 0.21 0.31 0.32 0.38 District of Columbia 0.02 0.07 0.02 0.03 0.02 0.02 Maine 2.40 0.12 0.01 0.53 0.18 2.04 Maryland 1.90 5.22 2.54 2.19 2.47 1.55 Massachusetts 5.73 1.58 0.12 1.44 1.29 4.13 New Hampshire 2.02 0.26 0.02 0.79 0.47 1.36 New Jersey 0.98 3.37 0.28 0.63 0.67 0.75 New York 5.06 4.92 1.24 4.67 7.20 4.03 Pennsylvania 7.19 10.97 11.71 8.86 11.16 5.65 Rhode Island 0.31 0.14 0.01 0.08 0.06 0.22 

MANE-VU 

Vermont 0.73 0.12 0.01 1.13 2.41 0.56 

MIDWEST 16.85 19.99 33.09 26.68 26.98 14.21 

Illinois 2.12 2.37 2.86 3.36 3.11 1.84 Indiana 3.67 4.28 6.52 5.83 5.57 3.19 Michigan 3.06 2.25 3.28 4.74 4.74 2.67 Ohio 6.31 9.07 18.33 9.82 10.66 5.07 

MIDWEST Wisconsin 1.69 2.03 2.10 2.93 2.90 1.44 

VISTAS 11.12 28.43 32.35 16.59 17.24 8.76 

Alabama 0.43 1.26 1.77 0.77 0.85 0.32 Georgia 0.65 2.13 2.12 1.30 1.41 0.52 Kentucky 2.05 3.23 5.29 3.39 3.59 1.64 Mississippi 0.01 0.03 0.04 0.03 0.03 0.01 North Carolina 1.84 6.03 3.20 2.52 2.51 1.42 South Carolina 0.61 1.87 0.75 0.80 0.71 0.49 Tennessee 1.10 2.19 3.27 1.72 1.80 0.86 Virginia 1.72 6.33 2.42 1.80 1.86 1.32 

VISTAS 

West Virginia 2.71 5.37 13.49 4.26 4.48 2.17 

OTHER36 33.79 16.78 12.70 23.54 18.99 44.17 TOTAL (μg/m3) 1.571 2.533 3.125 1.167 1.701 1.429 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU 

Table 8-4. Annual Average Sulfate Impact from CALPUFF (MM5) (%) 

RPO STATE ACADIA BRIGANTINE DOLLY SODS GREAT GULF LYE BROOK MOOSEHOR

CANADA 8.05 2.65 5.76 CENRAP 3.26 2.85 5.08 

Arkansas 0.23 0.32 0.39 Iowa 0.82 0.75 1.28 Kansas 0.43 0.29 0.65 Louisiana Minnesota 0.41 0.39 0.71 Missouri 1.01 0.80 1.48 Nebraska 0.10 0.08 0.15 Oklahoma 0.25 0.23 0.42 

CENRAP 

Texas 0.00 0.00 0.00 

MANE-VU 28.09 31.83 27.69 

Connecticut 0.43 0.27 0.25 Delaware 0.01 0.07 0.02 District of Columbia 0.60 2.81 0.73 Maine 1.62 0.06 0.14 Maryland 1.68 5.95 2.59 Massachusetts 8.67 1.87 2.18 New Hampshire 2.05 0.26 0.56 New Jersey 0.97 3.60 0.91 New York 4.41 4.30 8.08 Pennsylvania 7.44 12.57 11.86 Rhode Island 0.14 0.04 0.04 

MANE-VU 

Vermont 0.08 0.02 0.33 

MIDWEST 16.28 21.79 25.58 

Illinois 1.89 1.78 2.68 Indiana 3.57 4.46 5.48 Michigan 2.30 2.41 3.47 Ohio 7.53 12.11 12.30 

MIDWEST Wisconsin 0.98 1.04 1.65 

VISTAS 10.53 24.10 16.90 

Alabama 0.43 0.79 0.79 Georgia 0.78 1.74 1.30 Kentucky 2.02 3.47 3.22 Mississippi 0.01 0.03 0.04 North Carolina 1.72 4.23 2.53 South Carolina 0.50 1.10 0.62 Tennessee 1.07 2.04 1.82 Virginia 1.36 4.70 1.99 

VISTAS 

West Virginia 2.64 6.00 4.58 

OTHER36 33.79 16.78 12.70 23.54 18.99 44.17 TOTAL (μg/m3) 2.424 3.589 2.430 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU 

Table 8-5. Annual Average Sulfate Impact from percent time upwind method (%) 

RPO STATE ACADIA BRIGANTINE DOLLY SODS GREAT GULF LYE BROOK MOOSEHOR

CANADA 15.24 6.70 19.29 15.91 13.45 CENRAP 1.89 1.77 1.73 1.66 1.52 

Arkansas 0.12 0.24 0.15 0.15 0.15 Iowa 0.38 0.27 0.27 0.28 0.28 Kansas 0.00 0.00 0.00 0.00 0.00 Louisiana 0.04 0.08 0.06 0.04 0.04 Minnesota 0.56 0.33 0.38 0.44 0.44 Missouri 0.80 0.85 0.87 0.75 0.62 

CENRAP 

Texas 0.00 0.00 0.00 0.00 0.00 

MANE-VU 18.33 25.83 20.64 25.38 15.23 

Connecticut 0.51 0.27 0.52 0.59 0.40 Delaware 0.30 1.36 0.34 0.42 0.28 District of Columbia 0.12 0.29 0.11 0.14 0.12 Maine 1.49 0.08 0.68 0.26 1.53 Maryland 1.32 3.06 1.31 1.31 0.96 Massachusetts 1.10 0.33 0.86 0.81 0.90 New Hampshire 1.21 0.17 1.48 0.72 0.77 New Jersey 1.02 6.01 0.99 1.39 0.78 New York 4.80 3.49 6.80 9.08 4.23 Pennsylvania 6.21 10.71 7.10 10.36 5.07 Rhode Island 0.11 0.05 0.08 0.08 0.09 

MANE-VU 

Vermont 0.14 0.03 0.37 0.23 0.10 

MIDWEST 17.35 19.55 20.67 21.63 15.56 

Illinois 3.79 3.47 3.31 3.74 3.22 Indiana 3.37 4.36 4.33 4.13 3.21 Michigan 2.73 2.07 3.03 3.27 2.34 Ohio 6.10 8.65 8.73 9.23 5.77 

MIDWEST Wisconsin 1.36 1.00 1.28 1.25 1.02 

VISTAS 13.40 29.37 14.14 16.43 10.07 

Alabama 0.72 1.32 0.63 0.71 0.39 Georgia 1.40 3.21 1.06 1.54 0.72 Kentucky 2.65 4.71 3.59 3.83 2.31 Mississippi 0.04 0.10 0.06 0.06 0.03 North Carolina 1.29 4.35 0.92 0.99 1.18 South Carolina 0.72 1.64 0.42 0.41 0.44 Tennessee 1.05 1.91 1.04 1.16 0.86 Virginia 1.80 4.83 1.48 1.67 1.32 

VISTAS 

West Virginia 3.74 7.31 4.94 6.05 2.81 

OTHER36 33.79 16.78 12.70 23.54 18.99 44.17 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 8-7

MANE-VU will continue to explore these differences, but it remains encouraging 

that the use of different platforms and approaches results in more agreement across the 

various techniques than difference. With the few, specific exceptions mentioned above, 

it is relatively easy — using the normalized results from multiple techniques shown in 

Figure 8-1(a-d) — to identify those states that have the largest influence on sulfate levels 

at each Class I site. MANE-VU believes that this information can provide a solid basis 

for initiating consultation and planning efforts between upwind and downwind states and 

RPOs. 

Figure 8-1(a-d). Comparison of normalized (percent contribution) results using different 

techniques for ranking state contributions to sulfate levels at the MANE-VU Class I sites 

(a) Acadia National Park, ME, (b) Brigantine Wilderness Area, NJ, 

(c)Lye Brook Wilderness Area, VT, and (d) Shenandoah National Park, VA. 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 8-8

An alternative means of displaying the above results is in Table 8-6, which shows 

the individual state rankings produced by different assessment techniques for Acadia 

National Park, Maine. In the left-side column of Table 8-6, states are colored according 

to their average ranking across the different assessment methods. Those states that are 

ranked in the top five on average, across all techniques are colored red, while states 

ranked in the top six through ten are colored magenta, and so on for each group of five 

going down the left-side column. Through this color scheme, one can see how the states’ 

average ranking compares to their rankings under each individual assessment method 

given in the other columns of the table. The fact that all techniques tend to come to 

Figure 8-1(a-d). Continued 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 8-9

consistent conclusions about which states are top contributors provides some confidence 

that the source regions with the most influence on sulfate levels at MANE-VU Class I 

sites can be correctly identified. Note that the CENRAP states and several other states 

along the border of the analysis domain represent only partial state contributions. 

Table 8-6. Ranked Contributing States to Acadia Sulfate 

Average REMSAD Q/d 

CALPUFF 

(VT) 

CALPUFF 

(MD) E x RTP 

CANADA MA CANADA CANADA MA CANADA 

PA CANADA PA PA CANADA PA 

OH PA OH OH OH OH 

MA ME NY MA PA NY 

NY OH IN NY NY IL 

IN NY MA IN IN WV 

WV WV MI MI WV IN 

ME NH WV WV CENRAP MI 

MI MD IL ME MI KY 

IL IN GA IL NH CENRAP 

KY MI NC CENRAP KY VA 

CENRAP VA KY KY IL ME 

MD NC VA NH NC GA 

NH NJ MD MD MD WI 

NC IL CENRAP NC ME MD 

VA KY ME VA VA NC 

WI DE TN WI TN NH 

GA CENRAP SC TN WI MA 

TN WI AL NJ NJ TN 

NJ CT WI VT GA NJ 

SC GA NH GA DE AL 

AL TN NJ SC SC SC 

DE SC DE CT AL CT 

CT AL CT DE CT DE 

VT RI MS AL RI VT 

RI VT RI RI VT DC 

MS MS VT DC DC RI 

DC DC DC MS MS MS 

Yet one more way of combining the ranked contributions is shown in Figure 8-2, 

which summarizes the relative contributions of four RPOs, Canada, and “outside 

domain” regions to ambient sulfate concentrations at several Class I areas using four 

different assessment techniques. The techniques considered here include: tagged 

REMSAD modeling, two CALPUFF platforms (MM5-based meteorology used by MDE 

and NWS observation-based meteorology used by VT DEC), the empirical emissions 

divided by distance approach (Q/d), and emissions times residence time probability. The 

estimates of state-by-state sulfate mass contributions (μg/m3) from each method have 

been aggregated by RPO, both in terms of their absolute contribution (these values are 

displayed within the bars shown in the graphic) and in terms of their proportional 

contribution relative to other RPOs. It should be noted that the “outside domain” 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 8-10

contribution shown for each analysis method was derived exclusively from the REMSAD 

result. Averaging estimated impacts at the Acadia, Brigantine, and Lye Brook sites over 

the four assessment techniques utilized, MANE-VU states account for about 20 to 

30 percent of sulfate impacts in these three MANE-VU Class I areas, while the Midwest 

RPO and VISTAS states each account for about 20 to 25 percent of the total sulfate 

contribution at Brigantine and Lye Brook and about 10 to 15 percent each at Acadia. The 

CENRAP states, Canada and “outside domain” add the remainder. Although variation 

exists across estimates of contribution for different sites and using different techniques, 

the overall pattern is generally consistent. Relative contributions are somewhat reversed 

at Shenandoah, which is a VISTAS Class I area. There, VISTAS states and Midwest 

RPO states account for roughly 20 to 30 percent of overall sulfate impacts, with MANEVU states contributing roughly 15 to 20 percent. 

Figure 8-2. Estimated RPO contributions to sulfate concentrations at Class I areas 

using different assessment techniques 

0.73

0.65 1.03

0.58

1.20

0.89 1.37 1.07 0.68

0.56 0.83 0.59

0.81 0.56 0.83

0.96

0.24

0.40 0.60

0.49

0.63

0.61

0.94 0.58 0.46 0.57 0.77

0.52

1.05 0.94 1.22 0.88

0.18 0.26 0.39

0.45

0.77 0.87 1.04

0.84

0.29

0.36 0.51

0.42

1.36 1.11 1.49 1.25

0.02 0.05 0.09

0.05

0.04

0.07 0.10

0.05

0.04

0.07 0.12

0.05

0.06 0.08 0.09

0.07

0.18 0.19 0.29 0.35

0.24

0.08 0.11 0.20

0.27

0.12 0.17

0.28

0.19 0.08 0.11

0.26

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

REMSAD

CALPUFF(VT)

CALPUFF(MD)

E/D

Percent Upwind

Acadia Brigantine Lye Brook Shenendoah

MANEVU MIDWEST VISTAS CENRAP CANADA OUTSIDE DOMAIN
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 8-11

While the foregoing discussion has focused on quantitative methods for 

comparing contributions from individual states and regions, additional analyses have 

been conducted to verify and support these results using more qualitative means of 

identifying “regions of influence” for each Class I area. One such qualitative approach to 

synthesizing and interpreting the results obtained through different assessment techniques 

is illustrated in Figure 8-3 and Figure 8-4 below, which show a series of maps shaded to 

indicate different levels of contribution from different states and regions as determined by 

the analysis platforms already discussed. In these maps, states are shaded darker the 

higher they rank in terms of percent contribution to sulfate at a Class I site. For example, 

in Figure 8-3, states in a line from Indiana through Massachusetts are calculated to have 

the greatest impact on sulfate at Acadia. Overlaid on top of these maps are contours of 

Figure 8-3. Ranked contributions of states to ambient sulfate concentrations at 

Acadia National Park, Maine. 

Note: Shaded maps show contributions as estimated by REMSAD, Emissions divided by Distance, CALPUFF VT, and 

CALPUFF MD. Red and blue contours representing regions of high incremental probability (IP) and high clusterweighted probability (CWP) are overlaid onto the shaded state maps to indicate similarity of regional contributions as

calculated by these independent receptor-based methods. 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 8-12

Incremental Probability (red) and Cluster Weighted Probability (blue) of contributing to 

sulfate on the highest days. The substantial consistency in the patterns support and 

bolster the quantitative results. The importance of this finding is that the receptor-based 

results portrayed by the contours rely on methods that are completely independent of the 

source-based modeling approaches used to calculate the underlying ranks. This sort of 

internal consistency among approaches gives considerable strength to the weight-ofevidence approach that MANE-VU has adopted for identifying sulfate source regions. 

Figure 8-4. Ranked contributions of states to ambient sulfate concentrations 

at Brigantine Wilderness Area, New Jersey. 

Note: Shaded maps show contributions as estimated by REMSAD, Emissions divided by Distance, 

CALPUFF VT, and CALPUFF MD. Red and blue contours representing regions of high incremental 

probability (IP) and high cluster-weighted probability (CWP) are overlaid onto the shaded state maps to 

indicate similarity of regional contributions as calculated by these independent receptor-based methods. 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 9-1

9. CONCLUSION 

As MANE-VU prepares to implement the requirements of the Regional Haze 

Rule, a significant technical effort has focused on developing multiple analysis tools for 

assessing contributions to fine particle pollution and thus visibility impairment at Class I 

areas in the eastern United States. These analysis tools span the discipline of atmospheric 

science and include traditional Eulerian “source” or “grid” models, Lagrangian dispersion 

models, back trajectory receptor techniques, source apportionment models, and simple 

approximations based on empirical relationships between emissions and geography. 

A review of the literature and of recent monitoring data has yielded a conceptual 

model of visibility impairment in the MANE-VU region that attributes a dominant role, 

on the worst visibility days, to the sulfate component of fine particle matter. This model 

in turn suggests that the most effective near-term strategy for reducing fine particle 

pollution and visibility impairment in the East is to continue reducing anthropogenic 

emissions of SO2. Reductions in both NOX and VOCs should also be considered. Given 

that sulfate, in particular, plays a dominant role in causing visibility impairment 

throughout the East, MANE-VU has focused on multiple methods of apportioning the 

sulfate mass found in ambient air at Class I sites to contributing states and regions. This 

weight-of-evidence approach is intended to overcome large uncertainties that would 

otherwise undermine confidence in the results obtained using any one modeling or 

analysis technique in isolation. 

The assessment techniques described in this report use numerous approaches to 

develop ranked lists of individual state contributions to sulfate levels in MANE-VU 

Class I areas. When these results are normalized and compared, we find broad general 

agreement concerning the top contributing states at each site as well as some differences 

that suggest the magnitude of uncertainty inherent in these results. 

The conclusions that emerge from this report regarding the relative contributions 

of different upwind RPOs to downwind sulfate concentrations at MANE-VU Class I 

areas appear quite robust and the modest differences presented here relative to the 

preliminary results presented in Spring of 2005 are a further indication that the general 

patterns of contribution presented here are unlikely to change due to further refinements 

of the emissions and meteorological inputs. This suggests that the MANE-VU findings 

are sufficiently robust to serve as a basis for inter-RPO consultations and the regional 

haze planning process. Given that as much as 30 to 50 percent of the ambient sulfate 

found at northeastern Class I sites on hazy days appears to originate within neighboring 

RPOs, coordination and consultation is likely to be critical if MANE-VU is to achieve its 

visibility goals for 2018 and beyond. 
Tools and Techniques for Apportioning Fine Particle/Visibility Impairment in MANE-VU Page 9-1

Appendix A: Application of Trajectory Analysis 

Methods to Sulfate Source Attribution Studies in 

the Northeast U.S. 

Appendix B: Source Attribution by ReceptorBased Methods 

Appendix C: Chemical Transport Model Results 

for Sulfate Source Attribution Studies in the 

Northeast U.S. 

Appendix D: Development of Parallel CALPUFF 

Dispersion Modeling Platforms for Sulfate Source 

Attribution Studies in the Northeast U.S.